{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取并观察数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import  stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "dpath = './'\n",
    "data = pd.read_csv(dpath +\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "全都没有空值，有一些无用数据和类别特征，先将无用的数据丢弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data.dteday<'2012-01-01']\n",
    "uselessData = ['instant','dteday','casual','registered']\n",
    "train = train.drop(uselessData, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddf7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('value of cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xd9d6e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddf7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='season',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xdc2fe48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc2f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='weekday',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xe01f518>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VrLtyIQ/tOkg2r4Wc4sdwPmDLjgOVrUhyxfLXw3ltTSKtR8ODEemYsfLXz+bNbKH8dSLGyl8/m7TGYsSTWGyC4UHdckoLUtCKKLpyb2t8yvsd11xAKh4j5btx0pLaknG+99wr3HXthcxsT3J4uMA3d/+cjy4923fTRKac7tUiAgefeeCZqpT3zzzwjPYuEm9GCiUu/dXTqjaBvPRXT2NE9TClBSloRWjvImk0QTDBJpCa0pIWpKAVoTpv0mh0IyUyRkEroj0RZ0NPF0vnzyURM5bOn1veTyuhC4T4kc2Xat5IKaNVWpGCVsRwsUTvjgNVKe+9Ow5o7yLxpj0Rm+BGSn++0nqUPRjRkU6w8dH93Pnd5yvHEjHjk5cu8NgqaWXDhaByIzVaxql3x4FyGae4Ape0FgWtCJVxkkaTScd1IyUS0m1aRHtygjmtpOa0xI9sboI5rZyGrKX1qKcVMVII+PmbWf7uo0vobEswOFLkJ68NkEklNBQjXsRisH75IlXEEEFB6wiJGJwxO8Mn7t1ZtQmk5rzFl7ZEnFntyaqKGImY0aaMVmlBuhRHFCfYBLKokhjiiXOObKFUVREjWygxHXdoEDkWBa2IzASbQGa0CaR4ki2Uat5IZVXGSVqQglaEJr2l0Wg3bZExCloRMStPeo/PHtQuseKTSouJjNHOxRG5fJFC4CgGrmrSOxkz0ind2crUK5YC+ofyrO3dPZYc1NPFnI4UCWW0yvQxqa6BrsIRiXiMw7k8a7aMXSA2ruiiPaPdtMSP4UKpZkWM6y45hxkKWtJi1NOKGBwpcP3mnVUVMZbOn8s9K5fQqYoY4kHgHOfdvK0qgzURM378xSuImcatZdpQT+t4ZNIJTpuZZvun3lu5q73rsf3KHhRvVFpMZIx6WhHZXJH+bP6I6gNzMikFLvFiJF9kpBjwZrbAWXMyvNifZVYmSVsiRpvmWWX6mFRPSwPiESXnau4SW5qGwV2agwH5UsBND+7l/Fu2cdODe8mXgsn9hYtMM3UPWmYWN7NdZvat8OtzzOxJM3vezO43s1R4PB1+vT/8/tnjfsZN4fF9Zrasnu3VmhhpNPkJqrTkVaVFWtBU9LTWAs+N+/o24K+dcwuAN4CPh8c/DrzhnDsX+OvwdZjZO4EeYCFwOfC3Zla3omtaEyONRjdSImPqGrTM7EzgvwF/H35twPuBr4Uv2QR8MHx+Vfg14fcvDV9/FdDrnMs55/4T2A9cVK82Z1Jxbru6enHxbVcvIpNScVLxQzdSImPq3dP6EvCnQBB+PRd40zk3+td2EDgjfH4G8CJA+P23wtdXjtd4zwk3nC/x0K6DrLtyIftuvYJ1Vy7koV0HGc6rjJP4oT3eRMbUbXzBzH4HeNU5t9PM3jd6uMZL3TG+d7T3jP99q4HVAPPmzfuF2zsqZsZHLp7H4Eg5SKUTMT5y8TythxFvhgsldv6sv2prkid+8jqXLDhFi4ul5dRzUPw3gCvN7ANAGzCTcs9rlpklwt7UmcBL4esPAmcBB80sAZwE9I87Pmr8eyqcc3cDd0M55f14G51OxnhrxHHTg3srKe93fvgC0hldHMSPTCrOwl+exQ33PV05JzVkLa2qbldi59xNzrkznXNnU06keNQ593vAvwC/G75sFfDN8PnD4deE33/UlReRPQz0hNmF5wALgB31anc2X+LT9z9Tlan16fufIavhQfFkuBBwaGiEv/voEn78xSv4u48u4dDQCMOF4NhvFplmfKQffQ7oNbNbgV3AV8LjXwHuNbP9lHtYPQDOuWfN7AHgR0AR+CPnXN0iiDK1pNG0xWOcc3In/UN5OtIJ+ofynHNyJ20aGpQWNCVXYufcY8Bj4fOfUiP7zzk3Aiyf4P1fBL5YvxaOGd1PK1oyJ5sr0dmmwCVTL18KGMwVq4as1y9fRCoeI5FQ4JLWojM+ImZwxzUXVGVq3XHNBdpPS7wJHDWrtGhtsbQidR0iYlae+P7LD/1apc5bJhVX0BJvMul4zSHrTFqJGNJ6FLQiigHccN/TNbYm6fbYKmll2QmqvGdzRW2XIy1HQStCd7XSaGJm/O21F/LWuCrvJ2WSWjsoLUlzWhHZCUrmZFUyRzxJJWIUitVV3gvFgJSSMKQF6ayPSMSMDSsiJXNWdJHQpJZ4MlwosbY3UuW9dzfDBa0dlNaj4cGIYuDoffIA665cWNm5uPfJA3zsknN8N01alNYOiozRWR+RSSfY+Oh+7vzu85VjiZjxyUsXeGyVtLKhXJE17z+XZe86vXIjtf2HLzOUKzJDiRjSYhS0IrK5Us0LhBYXiy/tiTg975nH2i27K4uLN6zooj2h5CBpPZrTikjEoOc981j38LOcf8s21j38LD3vmYfmvMWX4WKp5s7Fw0XNaUnr0aU4ojjB1uZFlR8QTzSnJTJGQSsiM8EFIqMLhHiinYtFxihoRYwWzB1vtGCuiA+ZVJzbrl5UtQxD+2lJq1LQikjEqLm1uea0xJfhfImHdh1k3ZUL2XfrFay7ciEP7TrIsPZ4kxakMa+IQuDo3RFZp7XjANddcg5tvhsnLSkRs5rZg1rwLq1IQSuiI51g0VkncfpJbZjB6Se1seiskzTpLd5owbvIGF2JIwqFEu88/SQ+ce/Osbvani4KhRLplD4umXpa8C4yRlfhiHzgKnXegEqdt7tXLiHtuW3SmrTgXWSMzvgIrYmRRpOIQc9F81jbu7uq96/kIGlFOu0jtCZGGk1xXO9/fJV3LXiXVqSgFZGKWc2U95QytcQTLXgXGaOzPiKZiDOzDe5euYSOdIKhXJFkzEiqOKl4MrrgfXSeFcYWvGtOS1qNelo1DOSKrN68k/Nu3sbqzTsZ0NCgeKQF7yJjdNpHZAsl1kQK5q7ZspusdokVT8YveB+tiNG74wAFzWlJC9LYQkQmFa89f6A6b+JJh9ZpiVSopxWh7EFpNCriLDJGQStCFbWl0WRScTauqJ7T2riiS+ektCRzbvqNi3d3d7u+vr7jeu/gSJGv/ttPj6g+8LFL5itTS7wJAke2UCKTipPNl8gk48S0DEOml0md0OppRcRi8JGL55EOU7PSiRgfuXgeMX1SIiLeqesQkU7EODwMNz24t1Iy568/3FUJYiJTLQgch4byrNmyq3JOblyxmLkdKfW2pOXoShyRzZf4k/urU97/5P7dZLXhnnhSXoaxK7IMY5eWYUhLUk8rQgVzpdFkUnFOm5lm+6feW5lnveux/UrEkJakK3GESuZIoxnJl/jssvO5ceueyvDg+uWLGMmXVH9QWo6GByNiBndcc0FVevEd11yApg7El5Jz3Lh1T9Xw4I1b91Cahpm/Isei27SImMHMtgR3XXshM9uTHB4ukIiZgpZ4oyFrkTGT6mmZ2drJHJsOAgdvDhe44b6nOe/mbdxw39O8OVxAZd7EF1VpERkz2eHBVTWO/f4JbEfDCBw1h2IUtMSX9kS8ZpX3dm2XIy3oqOMLZrYC+Ahwjpk9PO5bM4BDtd/V3DLpCQrmpnWBED8SiRhzMqmqPd7aE3ESWjsoLehYg+LfB14GTgbuGHd8ANhTr0b5lA2HYo7MHizS2Zb02DJpZYlEjBlhkJqh81Ba2FGDlnPuZ8DPgKVT0xz/Ymbccc0FfOaBZyrpxeXsQWViiIj4Nqn0IzP7EHAbcCrlooYGOOfczDq2zZtUwvjLD/0aZ83J8GJ/llRCAUv8UsFckbLJ5sz+FfDfnXPP1bMxjSIZjzErE8MMZmWSqJMlPqn2oMiYyc7kvtIqAStmkCsGVSnvuWKgdVrijWoPioyZbNDqM7P7zWyFmX1o9F9dW+ZJMYC1W6oL5q7dspti4Ltl0qoyqQkyWlV7UFrQZIcHZwJZ4LJxxxzw4AlvkWdKeZdGk81PUA8zX6JTVTGkxUy2pxUD/sQ5d51z7jrg03Vsk1ejBXPHGy2YK+JDJhln44rFVYuLN65YTCapGylpPeYmUXTTzHY55xYf61ij6O7udn19fcf13pF8kcMjRdb27q5Mem/o6WJmW4K2lO5qxQ9lD0oLmNQJPdmrcMzMZjvn3gAwszm/wHubSjGA3h0HWHflwsreRb07DvCxS+b7bpq0sFjMKkOBGhKUVjbZs/8O4Ptm9jXKc1nXAF+sW6s8yqTjnJRJctrMNGZw2sw0J2WSmtMSr9TTEimbVNByzm02sz7g/ZS7cB9yzv2ori3zJF8occW7TueG+56uGh7MF0oaHhQvyuu0cqzZsnvcOq0u5nakFbik5Uy64qZz7kfOub9xzv1/0zVgARQCx9reSMp7724KKvMunmTzJdZElmGs2bKbbF7JQdJ6VCY6QhvuSaPRMgyRMXULWmbWZmY7zOwZM3vWzP5XePwcM3vSzJ4PFyynwuPp8Ov94ffPHvezbgqP7zOzZfVqM2jDPWk8WoYhMmZSKe/H9YPNDOhwzg2aWRL4N2At5TVeDzrnes3sy8Azzrm7zOwPgUXOuT8wsx7gfzjnPmxm7wS2ABcBvwx8FzjPOTfhX+zbSXkvlQIGckXezBYqBXNnZZLMSCeIx9UxlamXzRfpH8pz49Y9lTmt9csXMacjRUbzrDJ9nNCU91+YK0fDwfDLZPjPUU7m+Eh4fBOwDrgLuCp8DvA14G/CwHcV0OucywH/aWb7KQewJ+rRbjOjUAq46cG9VZPepqq54klbIs6MdKJq54EZ6QRt2rlYWlBduw5mFjez3cCrwCPAT4A3nXOjY20HgTPC52cALwKE338LmDv+eI33nHDl4qQ1Jr1VnFQ8icWMGW1JTp5RXoZx8ow0M9qSyhyUllTXoOWcKznnuoAzKfeOfrXWy8LHWn+B7ijHq5jZajPrM7O+11577XibrOKk0pBGFxfHLHxUwJIWNSWTNM65N4HHgIuBWWY2Oix5JvBS+PwgcBZA+P2TgP7xx2u8Z/zvuNs51+2c6z7llFOOu62jxUnHGy1OKuJLqRQwMFIgcI6BkQKlkrYdkNZUz+zBU8xsVvi8HfivwHPAvwC/G75sFfDN8PnD4deE3380nBd7GOgJswvPARYAO+rV7vZEjA09XVXFSTf0dNGeUBKG+DGaHHRoMI9zcGgwz0CuqMAlLameqUenA5vMLE45OD7gnPuWmf0I6DWzW4FdwFfC138FuDdMtOgHegCcc8+a2QPAj4Ai8EdHyxx8u4aLQe3ag785n05lD4oHuWLAYK5YlRy0fvkiUvEYGZ2T0mLqlvLu09tJeQ+c47ybt1EcVwEjETN+/MUriCmDUDwYHCly/ea+qv20ls6fyz0ru+lsU8q7TBuTusDqNi1Cc1rSaFQRQ2SMglaENtyTRqMqLSJjNDxYg7aBkEYyki9yOFdk7bgq7xtWdDEzrY1JZVrxWxFDRE6MVCJOJnDcde2FzGxPcni4QCJmpFQRQ1qQhgcjgqC8Dub1gRzOwesDufL6GG1NIp7EYkYmlSARZgom4jEyKS0wltaknlbESLHEQI304mQipuKk4s1oRQyg8ijSitTTiggCuHHrnqragzdu3UOgdZwiIhVB4BjMFQlc+DhFo1G6ZYvIpOOcNjPN9k+9t7K4+K7H9iu9WLxScpA0kiBwHBrKs2bLrnG7YSxmbkeq7uelsgcjsrki/dkaexdlUmQ0LCMe+LxAiNQymCty/aYaC95Xdb+d4WstLj4eJedqDg+WpmFwl+ZQ3i5nV2S7nF3aLke88bkbhoJWREc6UfN/Rod6WeKJtsuRRpPNTVA5KFf/GykFrQhVH5BGo9Ji0mhiMVi/fFFV5aD1yxcRm4KIojmtCM1pSaMZXTv4RrbAWXMyvNifZXYmqd2LxZvAOT59/25ueN+5VQlrd3646+0UFldFjOPRlopz+zf2VW1Ncvv2fdz54S7fTZMW5ZwjVwqq1g5uWNFFp5toY2+R+srmS7xyOMeyLz1eObZ0/lyy+VLd1xFqeDBi/P+Md/zZt1n2pcd55XBOQzHiTbZQYu2W3VWJGGu37FYihnjjs7C4eloRo/8zounFqvIuvig5SBpNLGbM7Uhxz6ruKV87qLM+wuf/DJFaRpODxq+JGU0OmtGW9Ngykamn4cEaRuu8xSx8VMASj9qTcTb0dFUNxWzo6aJdvX/xZHTB+/Wb+jjv5m1cv6mPQ0PhnzG6AAAcO0lEQVT5KSnlpOxBkQY3mCvy/CuHeccpM+hsSzA4UuQnrw2w4LSZKp4rXvisiKEzXqTBtSdinDErwyfu3TmWPdjTRXtCAyXihypiiMiEhosBa3sj2YO9uxkuausB8cPngncFLZEGpzJO0miU8t5gtA2ENJLRu9po9uBULOQUqcVnlrV6WhGjJXNeH8jhHLw+kGNgpDBlG5yJRPm8qxWZiK8sa92mRYwUSgzkilUlc9YvX0QyHlPtQfFCawdFxqinFRE4au6npY6WiIh/CloRmfQEk95pDcWIHxqyFhmjoBWRnWA/raz20xJPRopjQ9bn37KNmx7cy0CuyEhRBXPFn1IpKN88ufJNVak0NUswFLQiEjGrWTInofkD8SQIJhiy1jIt8aRUCjg0lGf15p2cd/M2Vm/eyaGh/JQELgWtiGIAvTsOsO7Khey79QrWXbmQ3h0H0DpO8UVD1tJosoVSzQXvU7FdjtLhIjLpOBsf3c+d332+ciwRMz556QKPrZJWpirv0mh8bpejnlbE0ARzWkOa0xJPMqk4t129qGrI+rarF6kihnjj8zqpoBWRjBkbVkTmtFZ0kdSclngyXAh4aNfBqiHrh3YdZLigMWvxIzPBdjlTseBdW5NEBIEjmy9SDBwz25McHi6QiBmZlPbVEj9G9y6K7qY9tyOlc1K8KZUCsoUSHekEQ7kimWScePxt9YO0NcnxiMWM9mS8MqEYD7/WxUF8UUUMaUTxeIwZYZCayrlVBa2IIHD0Zwu6q5WGMlrnDVCRXGlpmtOKyBZKrNmyqyqVc82WXVOSyikiIkenoBWhvYukEQWBYzBXJHDho0o4SYtS0IrwuSOnSC2jiRjXb+rjvJu3cf2mPg4N5RW4pCUpaEVo7yJpNBqyFhmjGd2IWMyY3Z7k7pVLKqmc7Qllaok/GrIWGaOeVkSpFNCfrS4E2Z+dmkKQIrWoSovIGC0ujhgYKbB6886qOm9L58/l7pVLVOdNvCgFAf2DeYbyJc6ak+HF/iwdqThzOlPEY7rvlGlDi4uPh89CkCK15AoBhcBx04N7K2sH77jmAnKFgExaQUtai874CA3FSKMJnOMzDzxTlYjxmQeeIZiGoyQix6KgFdGeqF0Isj2hSW/xIzNB7z+j3r+0IJ31EcPFEjt/1s9d115YKZj7xE9e55IFpzAjoRgvUy87wX5a2VyRTs2zSotR0IroSCf44y27KY5buJmIGT/+4hUeWyWtLBYz1i9fxI1b91TmtNYvX6RlGNKS1HWI0JyWNJq2ZJzbt++r2k/r9u37aNOCd2lB6mlFpGLGhp4u1vburtzVbujpIqW7WvEkmyvyyuEcy770eOXY0vlzNTwoLUnrtCKCwJEvligErlIRIxkzUqqKIZ5kc0WyhSKDI2PrtDrb4mSSCSVjyHQyqQushgdryBUDDg3mcQ4ODebJFVUNQ/xJJ2PkCuV1Wuffso2bHtxLruBIJ/XnK62nbme9mZ1lZv9iZs+Z2bNmtjY8PsfMHjGz58PH2eFxM7ONZrbfzPaY2YXjftaq8PXPm9mqerUZYKRYYiBXrLpADOSKjBRVnFT8yOZLfHZr9Tqtz259RjsPSEuq561aEfiMc+5XgYuBPzKzdwKfB77nnFsAfC/8GuAKYEH4bzVwF5SDHPAF4D3ARcAXRgNdPQQB3Lh1T9UF4satewjU2RJPVKVFGpGvPd7qFrSccy87554Onw8AzwFnAFcBm8KXbQI+GD6/Ctjsyn4AzDKz04FlwCPOuX7n3BvAI8Dl9Wp3Jj1BRe20MrXEj+wEGa1ZZbSKJz73eJuSQXEzOxtYDDwJnOacexnKgQ04NXzZGcCL4952MDw20fHo71htZn1m1vfaa68dd1t1gZBGk4gZG1ZEqrSs6CKhxCDxxOceb3UPWmbWCXwd+JRz7vDRXlrjmDvK8eoDzt3tnOt2znWfcsopx9dYygs577jmgqoLxB3XXKDMQfGmGEDvkweq1mn1PnkA5QeJLz73eKvroLiZJSkHrH90zj0YHn7FzE53zr0cDv+9Gh4/CJw17u1nAi+Fx98XOf5YvdrclozzV9/Zy7orF3LuqZ3sf3WQv/rOf3Dnh7vq9StFjiqTjrPx0f3c+d3nK8cSMeOTly7w2CppZdl8qXZpsXyJzjrPtdYze9CArwDPOefuHPeth4HRDMBVwDfHHV8ZZhFeDLwVDh9uBy4zs9lhAsZl4bG6yOZKlYWc7/izb7PsS4/zyuEc2ZwytcQPVWmRRpNJxtm4YnHViNTGFYvJTEGVlrotLjazS4B/BfYCowMZf0Z5XusBYB5wAFjunOsPg9zfUE6yyALXOef6wp/1sfC9AF90zv3D0X7321lcnM0V6c/mj6jzNieT0kJO8aIUBPy/t3J8duszlXPy9uUX8EsnpbUJpHgTBI5soUQmFSebL5FJvu0CDJN6sypiRATOce8TL3BV1xmVKu/f3P1zPrr0bGKmeS2ZeqqIIS1CFTGOx0i+xG+/85e44b6nOe/mbdxw39P89jt/iREt5BRPSs7xTz84UKnMkisG/NMPDlCahjecIseioBVRmmCXWF0gxJdMKs4HF5/Juoef5fxbtrHu4Wf54OIzpyRTS2Qi025xcbNS9QFpNNl8ic99vbpKy+e+vkdlnMSbIHAMjBR4fSCHc/D6QI6BkcL0WVzcTLK50gSLi3WBED90IyWNxmeNVp31ETGj9i6xysEQT7K5Emvefy7L3nV6Ze3g9h++TDZXorNNf8Iy9cbXaAUqNVrvWdld99+tM76GtmSMv/zQr1Uytdq0BYR4lIhBz0XzjtiYNKHTUjzxWaNVp31EWyrOrd96ripT69ZvPUebJr3Fk2IAa3t3V81pre3drTJO4o3PaRT1tCKGJtjafChXZIa2NhcPtPOANJpMKs7GFV2s2TLW+9+4oqv5aw82o/ZEnA09XUcMxbQndIEQP0bvao+o86Y5LfEkFjPmdqS5Z1X3iayIMSmqiBExmCvyrz9+laXvOLlSEeOJn7zOb553at0LQYrUUt67KHfEXe3cjrR2H5DpZFIns67CEZlUnO3PvsL8U2Ywoy3JK4dzbH/2FZa963TfTZMW1p6Mc9e1F1ZupLSXlvhWh9qDk6KgFTGSL/HZZecfkfI+ki+pzpt4MVIsMVIsMThSYkZbkjezBTrbyheITErnpEy90Z2L12zZNa73v5i5Ham6By5lD0YEzlXWH4xmat24dQ/BNBxGlSbhYKQQVC3kHCkENbZCFZka03rn4maTmaD6gHpZ4kvgmOBGynfLpFX53LlYQStCZZyk0SjlXRrN6M7F443uXFxv6j5EtCViNVPe21R+QDwZ3bk4mvKutYPiSyYZ58vXXsgb2UKlctDsTHJKdi7WlThiuFiid8cB1l25kH23XsG6KxfSu+MAw1NQCFKklvZkee3g+K3NN/R00T4FFwiRieRL1fOs+dLUlGjROq2IwDnOu3kbxXETBomY8eMvXqGdi8WLgZEC//Bv/3lEwdzrLjlHPS3xYjBX5PpNfVW9/6Xz53LPqu63s55V67SOh4ZipNF0pBNsfHQ/d373+cqxRMz45KULPLZKWpkSMRpIJhXntqsXVQ3F3Hb1Iu0SK974nPQWqcXnOanhwYjBkQJfrTEU87FLzqFTPS3xYHSX2Oik94y2pMo4iRd1Wlys4cHjETPjQ0vOrLEJpC4O4odzjlw46V3JaF3RRadzTPLvXOSEKhfMTalg7onydhMx7n3iBa7qOqNS5+2bu3/OR5eercAlXgyMFFi9eecRk953r1yieVaZTtTTOh4j+RK//c5f4ob7nq7c1d5xzQWqPSjedKQTnDYzzfZPvbcyZH3XY/vp0PkoLUg9rQjd1UqjyeaK9GfzRwxZz8mkdCMl08mkelrKHozomKD2oO5qxRcVcRYZo6AVkQ3XaY1Xrj1Y9NQiaXUq4iwyRkErImbG+uXV67SUPSg+DU1wIzWkGylpQQpaEW2pOLdv31dVe/D27fto0+Ji8UQL3kXGaHwhYihX5JXDOZZ96fHKsaXz56qMk3gznA94aNdB1l25sJI9+NCug3zskvl0tum+U1qLzvgI3dVKo8mk4qx4zzzWPfws59+yjXUPP8uK98zTOSktST2tiGy+VPOutlxRWzFe/EjFY/zlh36tUsYpFde5KK1JQSuiPRGn56J5R2wC2Z7QXa34kS2U+IP7nj7R20CINCWd8REjpYDABdy9cgkd6QRDuSJDuQIjpYBO7V4sHvjcBkKk0egqHNGWiBGzGKs37+S8m7exevNOYhajTQFLPNHWJCJjdCWOGC6UWNu7u6r6wNre3QwXdIEQPzLJOBtXLK5KDtq4YjGZpHpa0no0PBihMk7SaGIxY04mWTVkPVXbQIg0GvW0IlR9QBpNEDgGc0UODeZxDg4N5hnMFQkC1R6U1qOgFZFJxtnQ01U1FLOhp0tDMeLNSLHEQK7ITQ/u5fxbtnHTg3sZyBUZKWrIWlqPtiapoVQKyBZKVUMxca2LEU8GR4pcv7nvyJT3ld10tmnYWqYNbU1yvMwMCwvkjn8u4kMmPUHKe1q9f2k9CloRQeA4NJTn+k19nHfzNq7f1MehobzmD8SbbG6ClPechgel9Wh4MGIwV+T6TTWGYlR9QDwJAkc2X6QYOGa2Jzk8XCARMzKphDIIZTqZ1Mmsq3CEqg9Io3HOkc2Xjiwtlowzyb9zkWlDw4MRSnmXRpOdYMF7VgvepQUpaEVoaxJpNFrwLjJGZ33EcCHg0NAIf/fRJXS2JRgcKfKT1waY05miM60YL1NvtPc/fp51tPevjUml1egqHNEWj3HGrAyfuLdcMPcT9+7kjFkZ2rROSzyJm7F+eXXvf/3yRcS1FENakK7EEcPFCQrmqvqAeNKWinP79n2su3Ih+269gnVXLuT27fto05C1tCAND0Zo/kAaTTZX4pXDOZZ96fHKsaXz55LNlVQRQ1qOeloRyh6URhOLUXN4MKa/XmlBdVtcbGZfBX4HeNU5967w2BzgfuBs4AXgGufcG1auk7QB+ACQBX7fOfd0+J5VwC3hj73VObfpWL/77SwuLpUCBnJF3swWOGtOhhf7s8zKJJmRTqj+oHgRBI6BkQJvjDsnZ2eSzGhLanGxTCfeaw/+H+DyyLHPA99zzi0Avhd+DXAFsCD8txq4CypB7gvAe4CLgC+Y2ew6thkzo1AKqipqF0qB6g+KN7GY0ZFKMLczhRnM7UzRoWoY0qLqFrScc48D/ZHDVwGjPaVNwAfHHd/syn4AzDKz04FlwCPOuX7n3BvAIxwZCE+obKHEmi3ViRhrtmghp/hTKgX0Z/Os3lzOaF29eSf92TylUuC7aSJTbqrHu05zzr0MED6eGh4/A3hx3OsOhscmOl43KuMkjUYVMUTGNMokTa1xDneU40f+ALPVZtZnZn2vvfbacTdEiRjSaJTRKjJmqoPWK+GwH+Hjq+Hxg8BZ4153JvDSUY4fwTl3t3Ou2znXfcoppxx3A1XGSRqNbqRExkx10HoYWBU+XwV8c9zxlVZ2MfBWOHy4HbjMzGaHCRiXhcfqJpsv8dCug1ULOR/adZBsXkMx4kcyZmzo6aq6kdrQ00VSiRjSguo2vmBmW4D3ASeb2UHKWYD/G3jAzD4OHACWhy//NuV09/2UU96vA3DO9ZvZXwBPha/7c+dcNLnjhMok4/RcNO+IbSAySfW0xI9UMs62vhe569oLK/tpfXP3z/no0rN9N01kymkTyBpKpYBsoURHOsFQrkgmGdcaLfFGG5NKi/C+TqtpxeOx8sJNM2a0JRWwxKtMMs7GFYurhgc3rlis3r+0JN2miTS4WMyY25HinlXdZFJxsvkSmWRci4ulJSloiTSBWMwqQ4EaEpRWpnGvGoLAMZgrErjwMZh+834iIs1It2wRQeA4NJRnzZZdlezBjSsWM7cjpeEY8SYIHNlCScOD0vLU04oo1x7cFak9uEslc8Sb0Rup6zf1cd7N27h+Ux+HhvIaARCvfI1IKWhFqPagNBrdSEmj8XkjpeHBiGy+xJr3n8uyd53Ouad2sv/VQbb/8GWy+ZImwMUL3UhJoxl/IwVUbqSmYu2grsIR7YlYzYoY7Ql1SsWPbL7Eu8+eU7W4+N1nz9GNlHjj80ZKV+KI4WJQcxuI4aL2LhI/tLhYGs3ojdR4ozdS9abbtAgNxUij0eJiaTSZZJwvX3shb2QLnDUnw4v9WWZnklNyI6WeVoTPOwgRkWaRLwXc9OBezr9lGzc9uJf8FO2kraAVoaEYaTRKeZdGU07E2B3JaJ2a3bQ1PBihoRhpND4ztURqUSJGgxmt8xaz8FEBSzzSPKs0Gp/TKApaIg1O86zSaMrTKF2RaZSp2SxXm0CKNLggcAyMFI7I1JrRltQogHhRKgUM5Iq8Oe6cnJVJMiOdeDv7D07qZNaAuEgTGM3UGivi3OW7SdLCsoUSN9z39BG7ad+9cgkz6rxproYHRRqcz0wtkVo60oma86wdU5AYpKAl0uCUiCGNZihXrDnPOpQr1v13K2iJNDglYkijySTjbOipTsTY0KNEjOOmRAyZTrQxqTSiUikgWyjRkU4wlCuSScbfThIGKBFDZHrQgndpRGaGmR3xvN4UtESawOiCd0BVMMQ7n71/zWmJiMgvxOdu2gpaIiLyC1HtQRERaRqqPdhggsAxmCsSuPBRW0CIiFT43MJJM7oRSi8WETk6nxmt6mlF+JxgFBFpFr62cFLQilDJHBGRxqWgFaGSOSIijUtBK8LnBKOIiBydEjEiVDJHRKRxKWjVoJI5IiKNScODIiLSNBS0RESkaShoiYhI01DQEhGRpqGgJSIiTUNBS0REmoaCloiINA0FLRERaRoKWiIi0jQUtEREpGkoaImISNNQ0BIRkaZhzjnfbTjhzOw14Gcn4EedDLx+An5OvTVDO9XGE0NtPHGaoZ2t1MbXnXOXH+tF0zJonShm1uec6/bdjmNphnaqjSeG2njiNEM71cYjaXhQRESahoKWiIg0DQWto7vbdwMmqRnaqTaeGGrjidMM7VQbIzSnJSIiTUM9LRERaRotGbTM7Ktm9qqZ/XCC75uZbTSz/Wa2x8wuHPe9VWb2fPhvlcc2/l7Ytj1m9n0zu2Dc914ws71mttvM+urVxkm2831m9lbYlt1m9j/Hfe9yM9sXfs6f99jGG8e174dmVjKzOeH3puSzNLOzzOxfzOw5M3vWzNbWeI3X83KSbfR6Xk6yjV7PyUm2sRHOyTYz22Fmz4Tt/F81XpM2s/vDz+tJMzt73PduCo/vM7NlJ6xhzrmW+we8F7gQ+OEE3/8AsA0w4GLgyfD4HOCn4ePs8PlsT2389dHfDVwx2sbw6xeAkxvks3wf8K0ax+PAT4D5QAp4BninjzZGXvvfgUen+rMETgcuDJ/PAH4c/Tx8n5eTbKPX83KSbfR6Tk6mjQ1yThrQGT5PAk8CF0de84fAl8PnPcD94fN3hp9fGjgn/FzjJ6JdLdnTcs49DvQf5SVXAZtd2Q+AWWZ2OrAMeMQ51++cewN4BDjmYrh6tNE59/2wDQA/AM6sRzuOZRKf5UQuAvY7537qnMsDvZQ/9xPuF2zjCmBLPdpxNM65l51zT4fPB4DngDMiL/N6Xk6mjb7Py0l+jhOZknPyONro65x0zrnB8Mtk+C+aBHEVsCl8/jXgUjOz8Hivcy7nnPtPYD/lz/dta8mgNQlnAC+O+/pgeGyi4759nPId+CgH/LOZ7TSz1Z7aNN7ScIhhm5ktDI813GdpZhnKF/uvjzs85Z9lOMSymPKd7XgNc14epY3jeT0vj9HGhjgnj/U5+j4nzSxuZruBVynfGE14TjrnisBbwFzq+FkmTsQPmYasxjF3lOPemNlvUb44XDLu8G84514ys1OBR8zsP8Lehg9PA//FOTdoZh8AHgIW0ICfJeVhmH93zo3vlU3pZ2lmnZQvUJ9yzh2OfrvGW6b8vDxGG0df4/W8PEYbG+KcnMzniOdz0jlXArrMbBbwDTN7l3Nu/NzwlJ+T6mnVdhA4a9zXZwIvHeW4F2a2CPh74Crn3KHR4865l8LHV4FvcIK65cfDOXd4dIjBOfdtIGlmJ9Ngn2Woh8gwzFR+lmaWpHwR+0fn3IM1XuL9vJxEG72fl8dqYyOck5P5HENez8lxv/NN4DGOHHaufGZmlgBOojwUX7/P8kRMjDXjP+BsJk4e+G9UT3jvCI/PAf6T8mT37PD5HE9tnEd5nPjXI8c7gBnjnn8fuNzjZ/lLjK0HvAg4EH6uCcoJA+cwNum90Ecbw++P/rF1+Pgsw89kM/Clo7zG63k5yTZ6PS8n2Uav5+Rk2tgg5+QpwKzweTvwr8DvRF7zR1QnYjwQPl9IdSLGTzlBiRgtOTxoZlsoZxCdbGYHgS9QnmTEOfdl4NuUM7X2A1nguvB7/Wb2F8BT4Y/6c1fdbZ/KNv5PymPHf1ue96ToykUrT6PcjYfyH+E/Oee+U482TrKdvwvcYGZFYBjoceWzumhmnwS2U87a+qpz7llPbQT4H8A/O+eGxr11Kj/L3wA+CuwN5xAA/oxyEGiU83IybfR9Xk6mjb7Pycm0Efyfk6cDm8wsTnlU7gHn3LfM7M+BPufcw8BXgHvNbD/lANsT/jc8a2YPAD8CisAfufJQ49umihgiItI0NKclIiJNQ0FLRESahoKWiIg0DQUtERFpGgpaIiLSNBS0RBqQmf2+mf3yuK9fCBfAnujf820zmxX++8MT/fNFTjQFLZHG9PvALx/rRZMRViqoyTn3AVeudjCLcsVukYamoCVyApjZn5rZmvD5X5vZo+HzS83sPjO7zMyeMLOnzWxrWHcOM/ufZvZUuGfS3Vb2u0A38I/hnknt4a/54/D9e83sV8L3d1h5v7CnzGyXmV0VHv/98Pf8X8rFVU83s8dtbH+m3wxfN9qD+9/AO8Lvr5/Kz07kF6GgJXJiPA78Zvi8G+gM68tdAuwFbgH+q3PuQqAP+HT42r9xzr3bOfcuyqVyfsc597XwNb/nnOtyzg2Hr309fP9dwGfDYzdT3mvp3cBvAevNrCP83lJglXPu/cBHgO3OuS7gAmC0EsOozwM/CX/fjSfkExGpg5Ys4yRSBzuBJWY2A8hRriTeTTmQPUx5U7x/D8vvpIAnwvf9lpn9KZChXEPwWeD/TvA7Rgur7gQ+FD6/DLjSzEaDWBthOSDCPbbC508BXw0D6UPOuWjQEmkKCloiJ4BzrmBmL1CuB/h9YA/lns87KBewfcQ5t2L8e8ysDfhboNs596KZraMcdCaSCx9LjP3tGnC1c25f5Ge/B6jUrHPOPW5m76VcdPdeM1vvnNt8PP+tIj5peFDkxHmc8rDd45QrYv8B5WG4HwC/YWbnQnljPzM7j7EA9Xo4x/W7437WAOWt2I9lO+W5Lgt/9uJaLzKz/wK86py7h3KR0wsjL5ns7xPxSkFL5MT5V8qVsZ9wzr0CjAD/6px7jXI24BYz20M5iP1KmLV3D+U5r4cYq9IO8H+AL0cSMWr5C8oV6/eY2Q/Dr2t5H7DbzHYBVwMbxn/Tlfe9+vcwSUOJGNKwVOVdRESahnpaIiLSNBS0RESkaShoiYhI01DQEhGRpqGgJSIiTUNBS0REmoaCloiINA0FLRERaRr/P9srwyBBAdVHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe01f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='weathersit',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xe3aa2e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd838d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='temp',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x59f8828>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdde9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='atemp',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现季节/月份、天气和温度对y影响比较明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xebbbef0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aGqZw6KFY1W/NjC6MApKISFw0aukI9LD59XeTgeXJhfWUOR3cOXM8AH/xvRZueezH/NYlFcnEhVnXXs6KrfuZefVlzFr9EyY/uA3fEK64cLG0ZCciQ1YkEiXQE2sZ0RkM4zQmmdDw2I63aZg2hr+cPZnlW9L3j2pG+mg95QfOJi4k3sPZHkY6d3RhNEMSkSEpEonS3hli8cY9THpgG4s37qEzFGb0ME/ymrtvqmH5lv29atgt/NRVyWy6RMZd4v1Q62HUnxSQRGRICmQ58LpkU0va2aLEzCfVrqMdVJW7ePHA+8lU7+1vvs+axjpqRvlYv7Bee0cXSfNJERlyIpEoPk/2A6/jqr386L7PsXrHYY51BLI2zzvd3cOhh+ZwpruHqnIXX/30hGTLiEqPXudfLH3lRGRIiUSiBMNROoNhDj00h+33foaGaWOAWLB5+6Sf+58/wAO//0mqyp2smjet1+HXb/3gIFf/9Yvc9ewbdPVEh3TLiP6kGZKIlLTM5ndlBvzBcFq78USiwm3Tx7LqpUPJ5btHbp/K6h2HeeT2qYyr9uLvDvP0q+8kl+u0V9S/1DFWREpWtk6vaxrr2Pz6uzy24+3kdTMnVPOdO67jWz84mKy0kOj4ai10hSLJ4qgl3tk1X9QxVkSGtsy6dIkWEbOuvTztuliiQlmvsj9vn/Rzx5Ovg4l1dVVn1/xSQBKRkpVal67pD66h5Vu38i+LbmTM8HKa/uCa5HWJduOZe0Xb33xfy3IDSHtIIlKSolFLZyjM9eNHMGvKaL449XLuevaNs0t38+swBn745klWzq3l+3uPJ/eKOoNhvG5nWvac5J/2kESk5CT2jja9/mtumz6WYRUu7nr2jbT07ZkTqnliwQza/SFW7zjMydNBtRrPn5y+oJohiUjJSd07am3rZPX8uj6b7LX7Qzz2x3VKUigA2kMSkaKW2m7cHwwTjdq0vaPmfSc43dWTtcne6a4ebnnsxwBKUigACkgiUrQy240/9dMj+INhAHYs+yxNf3AN2+/9DJVuF2vmp7eUWDO/jhf2vhcrhBpUe/FCoCU7ESlaqUtzDdPGcNv0sXz9mT1piQubf/Eua19u5b831vHEghn4PC5Od/Xwg5b32H7wJI/Oq8Whl+YFQd8GESlaqUtzd99Uw4qt6ZW5l26OnTkKRy13/cteFm/cg787zEeBHu74nfE8cvtUqjwuyl1K6y4EmiGJSNHILAPkMCSLn1490kdTwxRqRlVy4qMuHAZ+65IKukIRGqaNoXnfiWQig8MYjIHLqjxKZCggCkgiUhSylQFa21jHkwvr+cnhU3QEQjQ1H0w+9ui8Wr75XAsnTwdZObcWgLYzQY51BLisypOstiCFQ98NESkKqftFACOrPHQGI1RXevjspFGcOhPk2a/dSOspP+t2trJ8y36aGqYwa/VPWLF1P4/cPhWX01DlcanyQoHK6x6SMWa2MeaQMabVGPNXfVzzZWPMW8aYg8aYf83neESkeKXuFzVMG8N//f1PAvG071CY+58/wOQHt9HUfJD7vjCZ0cM8yZbiiT5HI3xuqsrLtERXoPIWkIwxTmAdMAe4Bmg0xlyTcc1E4H7gd621U4B78zUeESle0ajF3x1OniVaMXsywUiU+58/wK/aOlm6qaVXm/F7b5lE6yk/ENtnipUD0lmjQpbPGdINQKu19oi1NgRsBr6Ucc0iYJ219kMAa+2pPI5HRIpUoCfC06++w8q5tcycUM0lFW6Wb4ll1PXVZnxctZfv/Kg1WSg10T5CClc+A9IVwLGU28fj96WaBEwyxrxqjPm5MWZ2HscjIkUoGrVg4f+9qYYRvjL+aWE9Xs/Z5bvWU/6sVRjOdIf5hy/X0dQwhRf2HqerJzoYw5cLkM+AlG1enFnJ1QVMBD4HNAJPGmOG93oiYxYbY3YbY3a3tbX1+0BFZOBlK/mT7Zr2ziBPvXKEEx9189Qr7/CBP8TJ33Qng9C6na3JmVOyCkNjHRtefSe5p9R44yeUyFAE8plldxy4MuX2WOBElmt+bq3tAd4xxhwiFqB2pV5krX0CeAJi1b7zNmIRGRDZU7h7V9qOZda10NQwhRVb9yffj6zysGreNO7bsg+HgTKn4V8W3UggGMEY8DgdfPXTE7jn5okqmlpE8hmQdgETjTFXAe8B84E/ybjmBWIzo6eNMZcRW8I7kscxiUgByEzhfu1IO0s27WX9wnq8Zc7k4ddEZl1inyjxPhyfTa2aV4vb5WDJppa0s0kVPg+VrtifN501Kh55W7Kz1oaBe4DtwC+B56y1B40x3zbGNMQv2w60G2PeAnYCy6217dmfUURKRWoKd8Kuox143c5ksdRl32vhTDyzLrFPlLpf1LzvBP5gbAaVmmG3ZFMLgR4VSy1GeT2HZK190Vo7yVp7tbX24fh937LWNsc/ttbaZdbaa6y1U621m/M5HhEpDIFQJGsiQmcwnJw53fW5GjbEM+u2v/l+2vvEflFfGXbKqCtOKq4qIgPOW+ZkbeP0tESEtY3T02ZONaMqWftyK6teOsSsay9nzPByFnxqPFdcWs7jd87g8MNzCITCWQNbIKQZUjHS4qqIDDiHw1Dtc8f2jOKFUitcDjqDkWSx1MTyXPO+EzTvi+VDzZxQzSO3T8XncVFpwed2sbZxelpyxJrGOipceq1djIy1xZW0Vl9fb3fv3j3YwxCRfubvDvPUK0e4bfpYVmzdz+hhHu6bNZnlW/anJSz44u0iEllzkUiUzlAEn8dF6yk/2998n8YbP9ErY08GVU7fCM2QRGRQRKM2lpLtcRIIRqhwOzjyQSfGkEzhDobDrF8wA6/HRXdPhGjUUl4Wu97hgHKXk65wlK8/syeZsQfw2pEO1i+sV4ZdkdF3S0QGXCQSpT0QYmlKuvZ37riOv5z923zzuX1pLSQqylzYqI0nPLSkPVblcVFZ7lJiQ4nQQquIDKho1NIZivQqiPpRoIdvPrcv7b7lW/bTE2/Kl5nevXzLfj4M9PSZsafEhuKjgCQiAyrQE9vvyZzVXDnCm3WmU+lxZb1+19EOrhzhxedxZc/YU6mgoqMlOxEZEJFINBmM3j4Zy6AbWeXh7ptqqBlVyZnunmSGXcL140fEOrxWevp+rMrTK2NPpYKKk2ZIIpJ3kUiU9s4Qizfu4e2TsUy4tfPr+MvZk2lqPsjkB7ex8WdHWTO/Lm2m8+i8Wi71luEw8A9fntbrseHesmTwqfS4km3JFYyKk9K+RSTvznT3sHhjLBOuYdoY7vvCZMqchmXxPaOEZbdM5L/83lX4PK60TLrEcwRCEUZfUp4solrucuB06nV1EcjpFYK+kyKSd6l7QM37TrDqpUOMvqS8177Q2pdb8SVmOuWuZIdXh8NQVV5GVUVZ7EIDFWVOBaMSo++miORdZzC9xE/zvhO82x7IKTsu0TcJA9Zaim1VR3KngCQi/aavpnveMmev/aHh3jLWNtadMzsu0Tdp0YbdTHpgG4s37uG9D7t56qdHaO8MZW3qJ8VLe0gi0i8S3V1TD6+uaaxjRIWb7kgUr9tJZzCcUrsuloyQ6H2ULTvOHwyzaMPutH2mmROqaWqYQlPzQVVjKB7aQxKRgRMI9T68uvn1d+no6j3D+edX3uHDrh6Ac2bH9dU3KdF2QtUYSosCkohclMzluQq3o1fwmHXt5b0qMqzYup9Z117Okk17z9tIr68qDIlK4KrGUFoUkETkgkUiUT7oDCZnPos27Ka9M8SSz9ekXddXA73UGc659oGy9U1KNOpTNYbSo8VXEbkgmbXoAF470s7STS08vmAGEEvfvn78CPzx7LrMCguJGc677QF8HlefrSIy+yYl9qC++ukJqsZQgpTUICIXxB8MU1HmZPKD2winzG5cDsOhh+bQ7g8ywufm+IddXOorIxiOplX1Xjm3lhf2Hue26WNZ9dIh2s4ElZxQ+tQPSUT6n9ftTNaiyzbzaWo+yPoF9VxW6cHhgEq3K22GU1HmZNa1l7PqpUM07zuBy2GUnCCAApKIXIBEIsP2N99n5dxaVmzdnzbzWfXSIXYd7aDC7cQYcJjYC+PKeEUFYwx3/tMvegWyQCiiGZIoIIlI36LxXkSJc0JYeLW1jT++YRzf+8W7fOeO66gqL6P1lD8545k5oTpZhTszyCSSFJZs2pvSllzJCRKjgCQiWSWqJKQGj2e/dgNTxgzne794l1nXXk6lx0V7Z5Cm5oPsOtqRrMJd5XFlDTKZSQpqFSGplNQgIkD6bCiRzfaBP4TDQEWZC6/HiT8Y5ulX3uGxHW8nP2/ZLRP5yu9eRWV5eoVuBRlJoUoNInJ+0aglEArT3hnkqZ8e4e2TfrxuF53BWHAKhqMs2hg7b/T1jXu4bfpYGqaNSX7+2pdbqfS4YvtAKRW6RS6UApLIEBaJRPEHw5SXOQlHLI03jGP7m+/TesqPz+PCAsu37O9VaeHum84egE2cN9I+kHxcCkgiQ1Q0amkPhPj6M3uY9MA2lj23Dwss/NT4ZBdXn9vVZ6WFROWENY11+NwXv0TXV4VwGXoUkESGqEBKtYXE7Gfp5hYwhpFVHsJRm6yokCqWph3m8MNzWL+wnst8notulJfZXiJRgkhBaWhSQBIZgqJRi9eTvZJ2pcfF8lmT2X7vZ7h6pI/VGX2MYjOivit0X4hAT4Qlm/amBcVciq5KaVLat8gQFOiJ8MGZYNZqCyc+6uJSr5sxw520nvJzpO0Mj985g8pyF/7u8MdansvUV3sJVW4YmjRDEhlColFLIBgGC2MvrWBNRsfWVfOmUeF2sGjjbiY/uI2m5oNMGTOcp199h85gmEqP66KX57Lpq72E2koMTTqHJDJERKOWM909nAmGWb4lVvLnvzfW8bs1I/F5XLSe8jPcW8a9m1uydmidOLoyWQqoP8eUefh2beP0Pqt/S9FScVUROSvQE+HDQA/3P38gGXC2vXmS+vEjqCx3ccXwiqxN9hJZdfmoN6fKDZJKS3YiQ4TX7eTKEd5kwGmYNob7vjCZJZtaYhluG3fzgb93k73rx4+IVW7I0zkjh8Ocs425DB0KSCIlLLFn5O8OA7FeRomAc/dNNazYmn7o9d7NLSz81FVZsuo0a5H8y+uSnTFmNrAGcAJPWmv/PuPxrwCPAu/F7/of1ton8zkmkaEi257R9eNHsGZ+HdB3e/HKcheP3D6VcdVeAvHyQQpGMhDyNkMyxjiBdcAc4Bqg0RhzTZZLv2etrYu/KRiJ9JPucCRr6Z+lm1tY8KnxdPWR4XasI9ZWHAuV5VpCk4GTzyW7G4BWa+0Ra20I2Ax8KY//nojERaOWzmCYqvKyrLOgqvIy/vr7B/iHL09LW55b21jHqGEeZbnJoMjnkt0VwLGU28eBG7NcN9cY8xngMPAX1tpjWa4RkQsQq4DQQlPDlD5bjTfvO4HDwPoF9Xg9ynCTwZfPGVK2n+rMQ0//Cxhvra0FdgAbsj6RMYuNMbuNMbvb2tr6eZgipSUatckKCOt2trJybm2vw6/rdrYCcPJ0EOKtxpXhJoMtbwdjjTEzgSZr7az47fsBrLWP9HG9E+iw1l5yrufVwViRviUSGYgHmNZTfl771QfMvPqy+FmiMP/8yjusfblVh1BlIA36wdhdwERjzFXEsujmA3+SeoEx5nJr7fvxmw3AL/M4HpGS1x2O0BmK8M3n9iWz6lbOreWFvce59IZxvHXiN8y69nLuuXmiluik4OS8ZGeMuc4Ys8QY8+fGmOvOd721NgzcA2wnFmies9YeNMZ82xjTEL9siTHmoDFmH7AE+MqF/xdEJCEahW8+t69XQ70FnxrP5l+8y+XDvTQ1H0xWXVAwkkKS05KdMeZbwDzg+fhdtwFbrLUP5XFsWWnJTqRvUWuZ9MA2win9hFwOw6GH5jD5wW0cemgOHZ0hLdPJQMvphy3XGVIjcL219m+stX8D/A7wpxc7MhHpP6kdVzuD4axnixKN9gKhsIKRFKxcA9JRoDzltgf4Vb+PRkQuSCQS5YPOYLLj6j+/8g5rMhrqrZxby/Y332dtorGegpEUqFyTGoLAQWPMvxNL3b4VeMUYsxbAWrskT+MTkT5Eo5bOlDbkAI/teBuAJxbMwOdxxYqiup189fcmqASQFLz1Y+PHAAAgAElEQVRcA9L3428JP+r/oYjIuUSjlkBPhHKXg66eCL54hezRwzxp1619uZV7bp6IwxiqyssAqCxXHWUpfDkFJGtt1gOrIjIwEo3sdh9tZ8YnRrB0c0syrfvRebVELTTvOwGc7bja372LRPItp5dNxpj/xxiz1xjTYYw5bYw5Y4w5ne/BiUhMrBTQXmZefRlL4x1dE2ndy7fsZ9mtk9LaReSrd5FIPuX6Emo1cDtwwBZbz3ORIpZYpqsoc9DUMIVhFdmLpY6r9nLooTl0BsPqXSRFK9eF5WPAmwpGIgMnUQaoMximvTNEU/NB3j7pz5rW3RkM09UTW6ZzOrVfJMUp1xnSXwIvGmN+TCzjDgBr7WN5GZXIEBeNWkLhCC6nA6/D4HW7aGqYwmu/+oCVc2tZsTW94Z63zKlAJEUv14D0MOAndhbJnb/hiEhiZmSJtRxP7fa6cm4tL//nSZoapjBxdGUsrVvBSEpErqWDdltr6wdgPOel0kFS6rpDYUIRS2W5i9NdPVR6XPyqrZN1O1tpOxOkqWEKTc0HWb+wXpl0Uiz6tXTQDmPMFz7GYETkPKJRSyAYJhyNtQ5/78MuNv7sKCc+6mb7m+9z3xcmM3qYh5pRlaxtnK5MOik5ub68uhv4S2NMCAgRi3bWWjssbyMTGUISy3RnMpboVs2bxtY9x5h17eWs2LqfR26fqnp0UrJymiFZa6ustQ5rbbm1dlj8toKRSD8JhMIEQhGWb9mfdsbovi37mHXt5dSMqkymd6senZSqXA/GGmPMHcaY/xq/faUx5ob8Dk2k9EWjlkAoTIXbyehLyrOeMaoZVXm2WncwomAkJSvXPaT/CczkbMdXP7AuLyMSGSIS5YBOnQ5ypjvMu+2BrGeM/N3heLXu6Xjd2jeS0pVrQLrRWns30A1grf0QpX+LXLRYpe4wI3xuLqv0MKy8jNU7DrNybm1a64i1jXX4PE6++ukJ2jeSkpdrUkOPMcZJrPUExpiRQDRvoxIpYbGZUZAlm84WSP3unTM4eTrIqpcO0dQwhZpRlRzrCOBzu3A5HVTqnJEMAbn+lK8l1n5ilDHmYeAV4JG8jUqkRCVmRks2pRdI3fBqrLFe25kgv7/2p9zx5Ot4PU7cCkQyhOR0MBbAGPPbwM3EUr7/w1r7y3wOrC86GCvFKrFnVF3pZtID2whHz/7uuRyGQw/NpisUxetx8t6HXXz/jeN89dMTdPhVSkFOa805/aQbY56x1t4J/GeW+0QkB4kWEusX1HP9+BHJLq8QS15oPdXJrNU/Sd7nchjuuXniYAxVZFDkuh4wJfWGMcYFzOj/4YiUpmjUgoVnv3YjYHl0Xnrywpr5dWx/8/20z0k02hMZKs45QzLG3A/8NVCR0ZCvB3ginwMTKRWp3V5nXn0ZwyrKsMC6P5nOJV43xzoCeN1OGm8cx2tHOpKJDioPJENNrsVVHwH+GzCJWMVviJUO+knfn5Uf2kOSYpBorOd1O+kMhmk9dYYrhnvTWo+vmV/HCJ+b7p5o8nxR4nMCoQjeMjXak5LRr8VVjwA/AX4INKW8F5EUkUiUM909YOCDM0GWfa+FxRv3cMWlXjb/4t20zLqlm1tiTfXKY6WAHA5DpceFw8TfKxjJEJNrQFoCXA/82lp7EzAdaMvbqESKUCQSpb0zxOKNe5j0wDbuf/4Ay26dzMgqD0s3tTDr2svTrt91tAOfMuhEknINSN3W2m4AY4zHWvufwOT8DUukeESjFn8wTFdPlKWb088Xrdi6n7tvqknWpEuVaD0uIjG5BqTjxpjhwAvAvxtjfgCcyN+wRIpDImFh0YbdVLidfRZHTQSfzMw6JS2InJXTeoG19g/jHzYZY3YClxDbRxIZ0gKh2Nmi1460JytyZ54vOtYRYOXcWrxlTh6/cwaV5S61HhfJ4oJ/G6y1P7bWNltrQ/kYkEixiERi5Ryf/dqNbL/3M7z2qw/6LI76wt7jBHoiuJwGLFSVlykYiWTQjqrIRYhEorQHQixNKZC6cm4tR9rO8J07rmNYRRmnu3owwDeefYM1jXX43JoRiZxLzrXsCoXOIclgyTxbtHjjnrTluZkTqvnunTP4xjN7Ug631uFzu8BAuUvnimTI6tdzSCJDWmrywqQHtuF1u7ImMFR6XGlZdks2tXDswy7+7OndtHeGYiWERCQrBSSRHCQKoyaCTSKBIVWsQKo/7b5Ell0sOO0l0KPadCJ9UUASyYE3I6V73c7WXgkMaxqzF0hNBKldRzvUglzkHPIakIwxs40xh4wxrcaYvzrHdX9kjLHGmPp8jkfkQiUOvQaCkbQZUfO+Exw88RGP3zmDww/P4fEFM7jE4+KP6q9MC1Ir59aybmcroOrdIueTtyy7eMvzdcCtwHFglzGm2Vr7VsZ1VcRKE72er7GIXIzEvtGSTXsZPczDo/NqWb5lP7uOdrDk8zXM+MQIvp6RwPDvb/3/rF9Qj9fjxN8d5ulX3+HFA+/HU8BVvVvkXPKZ9n0D0GqtPQJgjNkMfAl4K+O6vyVWSfy+PI5FJGeZ2XQjqzy80HKCqIVHbp/KuGovp7t6uOvZN5JZdokEhkdun4ol1vuo0uPiq5+ewD03T1T1bpEc5DMgXQEcS7l9HLgx9QJjzHTgSmvt/zbG9BmQjDGLgcUA48aNy8NQRWJis6IgS1LOF62aNw2ILdO9eOB9Dj00h6rysqxZduOqvbSdCWLiFbsT7cfVhlzk/PK5h5TtpWAy59UY4wD+Efjm+Z7IWvuEtbbeWls/cuTIfhyiSLpAKMySTekFUu/bso/ls2K1hBOlgPrKsnvvwy4uq/QoeUHkIuQzIB0Hrky5PZb0gqxVwLXAj4wxR4HfAZqV2CADLZG4ELUWryf7+aIrLq1g5oRqVs2bRpnTcPVIH6vn1/VKYPj+G8c51hFQ8oLIRcjnOsIuYKIx5irgPWA+8CeJB621vwEuS9w2xvwIuM9aqzIMMmBSExd2He2g5VtfyFogNRCM8MjtU1n5w/+keV/sddWyWybyxIIZeN0uWk/5eWHvcW6fMZYqj0vJCyIXIW8ByVobNsbcA2wHnMBT1tqDxphvA7uttc35+rdFchXoibDp9V/T1DCFmlGVdIXCadl0148fwaPzagmFY0kObWeCuByG68ePoPHGcXjLnHSFIkwcXckVwyfgcKhEkMjFUi07GdIi0SjvfdjNiq1n07kXfGo8HwV6uHKEl2MdAS7xlvE3PzhIzUgfCz41nqryMo51BBg1zIPXrWQFkRyolp3I+QRCEVZs3Z9MYnhsx9ts/NlRRvjcAFzqdfODve/RvO8Ea19upaq8jDuefB2fx0W5S8tyIv1JL+9kyIpGLV63M7lc13rKz7qdrax9uZV7bp7Iu+0BHvv3w8k9o+vHj6ArFGH9wnqdKRLJAwUkGbK6wxH83WGamg+m9TSqGenD3x2mzOlI2zNa01iH161AJJIv2kOSIcvfHWbRxt29eho9fucMXm1to3bscPzBWMKCvzusBnsiF097SCLn4vU4s/c0KncxZcxwRg8rp6n5IJ3BMJUel4KRSJ7pN0yGrM5gOGu1hTPdYVZs3Y8/GEsBdzqMlulEBoACkgwpaVUZ3E5WzZuWVm1h1bxp+OK9j4ZVlFGlbDqRAaOkBikpqZW6MytsZ1Zl2LHsszS3vJeWZbd1zzFmXXs5148fQWcwTFV5mWZHIgNEMyQpGYmA89RPj/D2ST8VZU78wTCRSBTo3Yb8sX8/zO0zxtLUfJDJD26jqfkgt00fy/Y332dNYx0+t0vBSGQAaYYkJSNRBui26WOTlRcS6dqX+Ty92pA37zvBjE8M54kFM/B5XPi7w3jLnMy69nKqfW4FI5EBphmSlAyvOxZMUisvvHaknc2vv0tnKAzAjmWfpWHaGAAapo3h5k+OZvHGPUx6YBtff2YPJ37TzfY336erJzqY/xWRIUkzJCkJ0ailMxRm4ujKtFlQw7Qx3DZ9LIs37kkrluowcO8tk1i+ZX9a19cVW/fz+J0zVK1bZBBohiRFL7F3tHjjHt4+md447+6banrNmJZv2c9Dt8Vakfd1DknLdSIDTwFJil5qssK6na2snFubTOWuGVWZNeh4PU5Od/VkPYek5noig0NLdlKUUtO7sTB6mAcgWQi1qWEKE0dXJg+/Zjbce/ukP5ZNN7+OpZtbkst5axuna7lOZJAoIEnRyTxPlNgXitpYQGred4K2M0EeuX0qo4Z5WNtYx5JNZ4POqnnT0jq/JrLsMs8ticjA0pKdFJ3M80SJfaFlt05KVlxYObeW1TsO43E5cDsdPHL7VA49NIdHbp+K23k24Kx9uRWfx4XDGCo92jsSGUyaIUnRyTxPBLF9oXHVXg4/PIdAMMJvukJMuMxHIBThG8++0auid1PDFJr3nYjtGQUjVJbrV0FksOm3UApeZjkghyHrvpC/O8zXnzmb3r1mfh0+jytr8KoZVcnMCdWsjfc4EpHBpyU7KWiJ/aJFG3Yz6YFtLNqwm85gmO/ecV1aUdQ1jXU8/eo7act4Sze39FnRO9H5tdrn0TKdSIFQgz4paP5gmEUbejfRW7+gHgzJWVNFmYPJD/6QcPTsz7PLYfjPv51NR2eoVyadSgOJDKicftm0ZCcFra/9Iq8nFoiwUOlx4e8jvftXbZ1sf/N9ZdKJFAEt2UnBikZtn0tub5/0s2jDbto7Q0SjFm+Zk7WN09OW8VbOrWXdzlZl0okUCS3ZSUFK7B1lVu9e8vkaFn7qKirLXbSeih1u/eqnJ1DpcSXr2XndscfW7Wyled+J2BLfwnoqPVoQEBkkWrKT4pV61qi1rZOmhilcPdJHRyDEN549m0m3en4dFWUO/MFY6wif20V7Z4im5oPsOtoRz6RT9QWRYqAZkhSkqLVMemBbWpLC9ns/Q1PzwV4JDt+9cwaVHhfdPRGstXg9LjqDYbxuJ109Ue0ZiQy+nH4BtYckBSkQivTaO+qrUGpVuQsb329aFO9ttHjjHjo6exSMRIqIluykIFW4HDx+ZywzLrFX1Fcm3bvtAS71ulm6uSWtt9GSTXu1dyRSRDRDkkERjVr8wTBRG3+fsjQXjVo6Aj18/Zk9TH5wG03NB5l/wzg8TsPaxrpemXSrdxymqiJ7RQZVYRApHnrpKAMuW7Xu1MOqqQkNQLLqwiO3T2X1jsM8cnusud57H3bx6PZDtJ0JJnsbZc6eAqGIZkgiRUIzJBlw2ap1L9m0l0BPrDFeX4dhrxzh5YWWE3xu1Y/40/WvU1Xuomakj1XzpvGDlvd6zZ6UXSdSXPTSUQZcn9UX3E78wTDY7MVTW0/5066vKi9j/g3jePHA+2w/eJLZ117O43fOoLJcFRlEipFmSDLgsmXQpVZfiESjvWY7j86LVV1Ivb71lJ+lm1v43ORRrG2so8rjwud2qiKDSJHSDEkGXKLMT+oe0sq5tax66RCvHWnnG8++wT8trE/WnzvTHSYSjdJ2JojLYdKuT/RBwqIAJFLkFJAkLzJ7GKUunzkchhHesmTAefukn1UvHUq2FN91tINyt5M/Xf96MmB9547rkgdgW0+dvX7mhGolLoiUiLwu2RljZhtjDhljWo0xf5Xl8W8YYw4YY1qMMa8YY67J53hkYGTrYXSmuwd/dzzNuzuMPxhm8cY9vH3ST1PzwWQwgrNni1KTHu569g1Od/Xw3oddNDUf5MUD759tsKfEBZGSkLeXlcYYJ7AOuBU4DuwyxjRba99KuexfrbXfjV/fADwGzM7XmGRgZKZtj6zycCYYZvmW/ckZz6PzahlZ5WHdzlZWzZvG1j3HmHXt5dSMqqQzGOb7e4+nPeeuox2MGV7BX3yvhaaGKdSMquRYRyBWxVtLdSIlIZ8zpBuAVmvtEWttCNgMfCn1Amvt6ZSbPqC4CutJVplZdHffVMPyLfvTZjzLt+zn7ptqAKj0OJl/wziamg8y+cFtfP2ZPdz8ydE0TBuTfI5Ei/K2M0F+f+1PuePJ1/F5XJS7NDsSKRX5XHi/AjiWcvs4cGPmRcaYu4FlgBv4fLYnMsYsBhYDjBs3rt8HKv0rkUWXmCH1VYOuZlQld99Uw4eBHu5//kDaQdjlW/bzyO1TefHA+8kkhp/9qk1p3SIlLJ8zpGx/KXrNgKy166y1VwMrgAezPZG19glrbb21tn7kyJH9PEzpb5nN8o51BLKmeR/rCFAzqpIrR3izBqxx1V4OPzyHJxbM4IpLy/n0pFGxdG6ldYuUpHwGpOPAlSm3xwIn+rgWYkt6t+VxPDJAHA5Dtc/N+oX1HH54DqOGeXqdK1o1bxoVbiddoUifAaszGMZhDFXlZTgdDgUhkRKXt35IxhgXcBi4GXgP2AX8ibX2YMo1E621b8c//gPgb6y19ed6XvVDKk7ZurkC/O1tU8DSK+lh1bxp/NYlHpwOnd0WKQGD2zHWWhs2xtwDbAecwFPW2oPGmG8Du621zcA9xphbgB7gQ2BhvsYjg8vhMPg8rrSme6+uuIkz3WHKnA7KnA7WL6jH63Hy3oddbN1zjK/+3gQqyxWQRIYKdYyVAZFthvR3fziVRRt3M2vKaP5w+lgqy8/2Prp9xliqPC6qysu0TCdS/NQxVgZXNGqTh2EDoQiBUIRvPtdCU/NB7vvCZLweJ6OHefj9qZfzYSAEgMflYMGnxrPjrZN849k3khXARaT0KSBJXsSqNQRZtDFerWHjbnoiUR744icZWeVhxdb9dAbD3P/FTxKMRLn/+QNMfnAb9z9/AH8wzK3XjFaDPZEhRgFJ8iJWraGl12HYzlCEu2+qSQYbn9uV9dDsJRXuZIM9ERkaVJFS8uJcTfYgcQ6pi3HV2c8geT1ONdgTGWI0Q5JzikYt/mC8KGowTDSaWxJMIJi959GxjgBdoQjfvWMGPz58ikAw3OcZpERLcxEZGhSQpE/Zqna3d4ZyCkoOB/zDl6f1arI33FvGgy8c4BvPxurVuRwmS+vxOnxuHYIVGWqU9i198gfDLNqwO62V+MwJ1axfWH/e/kNRa3nmtaPcNn0sVeUuAsEIXT1hLvW6qXlg29nnWlAf65nUR+8kESkJg3swVopftn2g0cM8YEmmcmcGj0RjPgdw8ydH841n9qS1nDh5ujt5bWKvKFGbDlCjPZEhTEt20qdE1e6EhmljuG/W5LOp3BlLeKlLfO2doazZc6mrfcqiE5FUCkjSp8yq3ctundQryCzZtDd5eDW1Md+Y4RVZs+fGDK9I2StSFp2InKX1EelTatXuxAHVrCna8cfKnQ6eWDADn8dFZzx7LnX/KTYjCnP44TnaKxKRXjRDknNyOEyyB1HmEh7Eg0wwQigUpqMrxOKNe5j0wDb++ZV3WDM/M3tueix7Tv2MRCQLzZAkmYhwviy3xBLekk17k4kKaxvrAEswCkvjlRkAHtvxNgDfueM6hlWUaUYkIueltO8hLBq1dIcjdAbDLNnUkhJkpvd5KDU1eHUGw3jdTs50hxlWUZbWWgLA5TAcemgOxoDDKBCJDGGq9i19S2TEnTod7FVzbsmmvQRCkawVGhwOg7fMSbs/tjz3zef28VGgB4Adyz5Lw7QxyX8jUZlBmXQikgst2Q1RiYy4Z792Y5+15P50/euMHubh3lsmMa7ai787nDzEumTTXkZWeVh262RWbN2fdtbIYeDk6SCPzqulyuNSJp2I5EQBaYhKHHo98VFX1my4dn+QVfNqcbscvZbzRvjK2HW0g/+z5NOs2Lo/+bmJs0brF8S60DtMrL+R9o1EJBdashuiEhlzDgOPzqvtVXOuosxFT8T2uZx3/fgR1Iyq7HN25TBQXubE6dSPmIjkRn8thqhExtxvXVLBqu2HaGqYwqGH5tDUMIVV2w9R4XZy5YjsrSF8HhdrG6dzrCPQZ6XucmXUicgFUkAaohKHXgOhMCdPB5m1+idc/dcvMmv1Tzh5OkjrKT+tp/zZzx2FIlT73Iwa5ulVqXvl3Fr++ZV3cq4KLiKSoLTvEpTruaLEte2dobSzRWsa69j8+ru0tnVy3xfSkxYyU8KjUUtnKIzX7aL1lJ91O1tp3nci56rgIjIk5LRcooBUYrIFmLWN0xnhLaMrHE0LUhDLtit3OugKR/B5XJzu6uGdD/xcMdzL0s0taVl2gWAsyGUGt6i1Wc8gHX54js4fiQjoHNLQlFrgNDURoTMUSWu0d6a7h/bOIE/99AjtgbMlf+569g2qfeXs+XUH37njOv7hy3UAdPdEqCzPXu6nz5JCOn8kIhdAAanEZOthlEhESASpkVUeLDDC52HBp8Zz7+b0TLoVW/czYWQVVeVlXP3XL3LLYz+mPOMsUWprcyx8947retWt0/kjEbkQWuAvMYnZSua5otZTfiDe0+gLk7nr2TfYdbSDQw/NyRrAakZVJj8nMdtJ7AdlXxas45++Uk95mbq+isjF0QypxGT2MJo5oZo1jXUcaTvD9ns/w9/94dTkYdZw1PaZSecPhvnOj1qzznayLwu2ELWokreIXDTNkEpMZg+jQCiWtDDjEyNYurmlV6mgdTtbWTm3Ni2Tbs38OsKRCP/w5ToCoXCsZURKgOlrWTDRF0lE5GIoIJWgRAHUQOhsVe6l8X2ixIwosaTXvO8ENSN9ycZ677YH+LsXf8nJ08E+q373tSyYuqwnInKhtGRXghJ7PImsOq/blZzRJGZEqUt6jTd+Ap/bBRYuq/Lw2B/XsX5hfZ8tKLItCyqJQUQ+Lr2cLTGJg6ojfG6aGqawbmdr2qyoed8JAB65fWrsbFFGAkJihnOumU62ZUElMYjIx6WAVEKyZb+tnl9Htc/Nd++YwYafvcPal1tpOxPE54nNiC52iS3R2hwu/jlERFKpUkMJ8QfDLNqwO21vZ+aEapoaptDUfJA1jbHg1BWKZq24ICKSJ6rUMNT0lf1WM6qS1460s3RTC6dOB8GgYCQiBUcBqYT0VcInccB119EORl9SrvRsESlIeQ1IxpjZxphDxphWY8xfZXl8mTHmLWPMfmPMfxhjPpHP8ZS6bNlvK+fWsm5nKxBPzQ5GkjXmUsv/+INhtYsQkUGVtz0kY4wTOAzcChwHdgGN1tq3Uq65CXjdWhswxtwFfM5a+8fnel7tIZ1bZuuJM109jBpWzrGOAJXlTirKXFTE07OzVQXvK9VbRORjGPQ9pBuAVmvtEWttCNgMfCn1AmvtTmttIH7z58DYPI5nSEhkv9moJRAKs+y5fUx+cBv3P3+AqAWP04HDYdLK/3xx6uU0NUxhhM9NZ0gzJREZHPkMSFcAx1JuH4/f15c/A7blcTxDSlc4ytJN6VW8l25qoTsSBc4mQCSKrTY1H2Tyg9tYvHGPur2KyKDIZ0DKNkXL+lfOGHMHUA882sfji40xu40xu9va2vpxiKXrfPXmEgkQd99Uk1ZsNdE/KdCjXkYiMrDyGZCOA1em3B4LnMi8yBhzC/AA0GCtDWZ7ImvtE9baemtt/ciRI/My2GJwIUkI52ual0iAqBlVqUKpIlIQ8hmQdgETjTFXGWPcwHygOfUCY8x04HFiwehUHsdStDKD0FM/PZLs+nqupbVYwKnLqDdXl6w3lyj/EwiF1e1VRApCXis1GGO+CKwGnMBT1tqHjTHfBnZba5uNMTuAqcD78U9511rbcK7nHEpZdtlKAa2cW8uqlw7RvO8EMydU809fqY9fC16Pk0AwkpzdnOnu4cNAD1eO8HKsI8Cl3jKqysvSsuiyN9tTtp2I9Kuc/piodFABO1cpoFmrf8JtdWP4/xqmcCYYZvmW/WndW30eF3/2dO/PXb+wvlftucxUcRVKFZF+Nuhp3/IxnasUEMC9t0ziw0APy7dkJiW0EI2S895QIlVc3V5FZDApIBWwc5UCmjmhmnHVXq4c4c0eeDxO7Q2JSFFRQCpg2UoBrWmso2aUj3/6Sj2dwTDHOgJZA09nMMx377hOTfREpGhoD6nAZdvfgVjZn02v/5rGG8YRjETT9pBWzq3lhb3HabxxHD6Pi/Iy7Q2JyKDK6Q+POqsVuGyN8PzBcLLsT2tbJytmT+aJBTPwul20nvIns/BeO9LB+oX1yb0hEZFCpr9SRSg12aF53wma953gV3/3RSY/uI1wyrkkHXAVkWKiPaQilC3Zoa+9JCUxiEixUEAqQtmSHS71lmWpzKAkBhEpHkpqKFJ9JTvogKuIFCAdjC0lmTXtgF6HWXXAVUSKmQJSEUjUm1u0YTeTHtjGUz89otbjIlJyFJCKQGZ319umj+Xrz+xh2fda+OBMEAz4uxWYRKS4Ke27CKSmeSca6o2s8rDs1sms2LpfVbpFpCRohlQEUtO8Ew311OlVREqNZkgFIC1jLhjB4SCt3I+3zMl377iODwM9GAM7ln2276KqOggrIkVKM6RBlpmwsGjjbjo6Qyz7XkuyK6y1llAkyv3PH2DSA9u4//kD+IPq9CoipUUBaZClJiwklt6Wb9nPXZ+rSVuGW7KpJe2aDa++wxodhBWREqIlu0GQukR3viZ8u4524PO4el2z9uVW7v58DesX1usgrIiUBM2QBljmEt277dlr0LWe8ic/7uxjea6rJ6qDsCJSMhSQBljmEt1j/36YR+fVpi29PTqvlu/8qDVtGS6zdp2W50Sk1KiW3QCLWsukB9LbRNxWN4aHbpuK15M9y87hMFlr12lGJCJFQrXsClG21hEnT8eqLTiMobLchdfdexlOdepEpNQpIA0wLb+JiGSnLLsB5nAYqn1uZceJiGRQQBoEieU3IPleRGSo05KdiIgUBAUkEREpCApIIiJSEBSQLlJmS3E1xxMR+Xi0o34REuV/lmza+7Ga4+mwq4jIWZohXYRsFbovtDler7YT8VYTmmmJyFClgHQR+qrQfSHN8fojqImIlBIFpIuQrfzPhTbH64+gJiJSShSQLkJ/lP/pj6AmIlJKVO37In3chIT+SowQESkCOf1Ry2tAMsbMBhILx+QAAAfkSURBVNYATuBJa+3fZzz+GWA1UAvMt9b+2/mes1ACUn9Qlp2IDBGD237CGOME1gFzgGuARmPMNRmXvQt8BfjXfI2jkKmlhIjIWfncQ7oBaLXWHrHWhoDNwJdSL7DWHrXW7geieRxHv9OhWBGR/pfPgHQFcCzl9vH4fUVN54dERPIjnwEp2/rTRf3VNsYsNsbsNsbsbmtr+5jD+nh0fkhEJD/yGZCOA1em3B4LnLiYJ7LWPmGtrbfW1o8cObJfBnexdH5IRCQ/8hmQdgETjTFXGWPcwHygOY//3oDQ+SERkfzIW0Cy1oaBe4DtwC+B56y1B40x3zbGNAAYY643xhwH5gGPG2MO5ms8/aU/DsWKiEhvOhh7EXR+SETkguT0B1LtJy5C4vwQkHwvIiIfz5CtZaezRCIihWVIvrxXHTkRkcIzJGdIOkskIlJ4hmRA0lkiEZHCMyQDks4SiYgUniEZkHSWSESk8AzJpAaHw1Dtc7N+Yb3OEomIFIghGZBAZ4lERArNkFyyExGRwqOAJCIiBUEBSURECoICkoiIFAQFJBERKQgKSCIiUhAUkEREpCAoIImISEFQQBIRkYKggCQiIgVBAUlERAqCApKIiBQEBSQRESkICkgiIlIQFJBERKQgKCCJiEhBMNbawR7DBTHGtAG/HuxxZLgM+GCwB3EeGmP/KYZxaoz9Q2PsHx9Ya2ef76KiC0iFyBiz21pbP9jjOBeNsf8Uwzg1xv6hMQ4sLdmJiEhBUEASEZGCoIDUP54Y7AHkQGPsP8UwTo2xf2iMA0h7SCIiUhA0QxIRkYKggHQBjDGzjTGHjDGtxpi/yvL4Z4wxbxhjwsaYPyrQMS4zxrxljNlvjPkPY8wnCnCM3zDGHDDGtBhjXjHGXFNoY0y57o+MMdYYM+BZTjl8Hb9ijGmLfx1bjDFfG+gx5jLO+DVfjv9cHjTG/GuhjdEY848pX8fDxpiPCnCM44wxO40xe+O/318c6DF+bNZaveXwBjiBXwETADewD7gm45rxQC2wEfijAh3jTYA3/vFdwPcKcIzDUj5uAH5YaGOMX1cF/AT4OVBfaGMEvgL8j4H+ObyIcU4E9gKXxm+PKrQxZlz/58BThTZGYntJd8U/vgY4Opjf+4t50wwpdzcArdbaI9baELAZ+FLqBdbao9ba/UB0MAZIbmPcaa0NxG/+HBhbgGM8nXLTBwz0Rud5xxj3t8B/A7oHcnBxuY5xsOUyzkXAOmvthwDW2lMFOMZUjcCmARnZWbmM0QLD4h9fApwYwPH1CwWk3F0BHEu5fTx+XyG50DH+GbAtryPqLacxGmPuNsb8itgf/CUDNLaE847RGDMduNJa+78HcmApcv1ez40v3/ybMebKgRlamlzGOQmYZIx51Rjzc2PMeU/097Ocf2/iS9xXAS8PwLhS5TLGJuAOY8xx4EViM7miooCUO5PlvkJLUcx5jMaYO4B64NG8jijLP53lvl5jtNaus9ZeDawAHsz7qNKdc4zGGAfwj8A3B2xEveXydfxfwHhrbS2wA9iQ91H1lss4XcSW7T5HbPbxpDFmeJ7HlepCfrfnA/9mrY3kcTzZ5DLGRuBpa+1Y4IvAM/Gf1aJRVIMdZMeB1FeYYym8KXFOYzTG3AI8ADRYa4MDNLaEC/06bgZuy+uIejvfGKuAa4EfGWOOAr8DNA9wYsN5v47W2vaU7+96YMYAjS1VLt/v48APrLU91tp3gEPEAtRAuZCfyfkM/HId5DbGPwOeA7DWvgaUE6tzVzwGexOrWN6IvYo7Qmy6nthUnNLHtU8zOEkN5x0jMJ3Y5ujEQv06po4N+ANgd6GNMeP6HzHwSQ25fB0vT/n4D4GfF+j3ezawIf7xZcSWpqoLaYzx6yYDR4mf3yzAr+M24Cvxjz9JLGAN+Fg/1v9zsAdQTG/EpsGH43/QH4jf921iMw2A64m9kukE2oGDBTjGHcBJoCX+1lyAY1wDHIyPb+e5gsFgjTHj2gEPSDl+HR+Jfx33xb+Ovz3QY8xxnAZ4DHiL/9veHbNGEUZRGH4PWFhYBAQLBQloIRYhICwIYiVpLGxFrIQ01gpiIRELA/oHBDvR3sJCRDsRDKiQFIKIf0ArA3Z7LWYCGytXnd1PfR+YYoZhuDPLcvh2h3thEzjfWo39/hqwPo9n+JPP8Tjwsv+83wEr86r1Vzc7NUiSmuB/SJKkJhhIkqQmGEiSpCYYSJKkJhhIkqQmGEjSAJIsJLk87zqkv4mBJA1jATCQpCkYSNIw1oEj/fycO0muJtnoG53eBEiymOR9kvtJtpI8THKmbzL6IcmoP28tyYMkL/rjq3O9M2kgBpI0jGvAx6paBp7R9WYbAcvAiSSn+/OO0nWmWAKOAReAU8AV4PrE9ZaAs8BJ4EaSg7O4CWmWDCRpeCv99hZ4Qxc8O81DP1XVZlWN6dr8PK+ufcom3cDHHY+r6ltVfaZrAzSaVfHSrOyZdwHSfyDA7aq6t+tgsghMdlsfT+yP2f39/LHHlz2/9M9xhSQN4yvdmAqAp8ClJPsAkhxKcmDK651LsjfJfrq5QRt/rFKpEa6QpAFU1Zf+5YQturEAj4BXSQC2gYvANEPeXgNPgMPArapqbRaX9Nvs9i01LskasF1Vd+ddizQkf7KTJDXBFZIkqQmukCRJTTCQJElNMJAkSU0wkCRJTTCQJElNMJAkSU34Di6Bjb7En9TeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebbbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='temp',y_vars='atemp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "温度和体感温度冗余"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xee7d630>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xee7d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train, size=6, x_vars='windspeed',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'2011&2012 atemp')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf15d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.atemp.values, bins=30, kde=False)\n",
    "plt.xlabel('2011&2012 atemp', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'2011&2012 windspeed')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6aa080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.windspeed.values, bins=30, kde=False)\n",
    "plt.xlabel('2011&2012 windspeed', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 12)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=train.columns \n",
    "train_corr = train.corr().abs()\n",
    "train_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf6b36d8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf8469e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(train_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(train_corr, mask=train_corr < 1, cbar=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 1.00\n",
      "season and mnth = 0.83\n",
      "atemp and cnt = 0.78\n",
      "temp and cnt = 0.77\n",
      "weathersit and hum = 0.58\n",
      "season and cnt = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = train_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (train_corr.iloc[i,j] >= threshold and train_corr.iloc[i,j] < 1) or (train_corr.iloc[i,j] < 0 and train_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([train_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xffa3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10264eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104e84e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7E1edf4ZllWAtakanPNKwQKNFCKlIrtXTRe8/Bbds7PNkgjTqYHHtR87G8g0G5x/JmIbHEnETLyZe3pjYMQbH0xo0Bdx99XnYded89CyYhXuf3oVD76UsGZ5yfQ4rfrbB1uZieJAQUpbCVk9T3tfq2QRZ2PsvH54zm6DjUfzNQ1sMw2NmnTKSI5l82C4XBmxtiiA5kkFbPAYo4NQJcUvXmmiO4o5/fRUrPj0T13wnO45ll3bgzqvORaI5ikE9jGqmVjS8VgOxhlGo0sr11RP1d0WENBB2ZNLVSqoLWz19/cpZnk6QpcrGQROV3t6jSVN1YM6L2bDlbcNcXC4MuGHL27jq/DNw++PFuS9NId8txOxakyMZHHovhXuf3oWeBbPw/kltGBgawU2Pbssfa83iLmx8aS/WPttvmHcqp+Isl7fKXV/pe2HrHWkVhgcJCSl2ci61dBvPqe9u/kQHxrc04YFrZmPFp2bYDmM5gVnT2/s27y7ar9AjikQE7YkmLJ5bmosbQXLkRFPbeedOwe2Pj819rfj0OflzPXDNBYDK1mUNDqeRHMmGJKGAB665AEeOpfCXa3+N/e8ex/KSMOryjX2Yd+6UqsKq5UKVTnSjDxP0tAgJKVbqj6rZtxQj9d2axV24+dIOHB/VPC2CNQqjRQQ49F6qaL+cR5RbrBEK+VwYkLv+Ptx99XmYNjGB3701gI7J4wxDj9MmJrD7rvkYHs1gKJXGsh+8XOSJ3bspm7ta292F7147By26ATc6VsfkcUWvrYZVK+WtrNba1QP0tAgJKXYS8Hb2Nar5KVXfLd/Yh+Mjmi+LF5YuF9ISMxYxtMYiODo0gu/9+g1TMcbU9gT2Hk3iQ2e155vbFpIzfhERaAqG63X1LJiFH/ztXAylsl5TRMRUDdh/eHDMsa3gdK/EMEOjRUhIsTORWd3XKIyYiMeqUt95hVl47Hhay+epcoapkJwRuW/zbqzt7sKm1w6OKRBe292FiGTDgWaGf3xLE2bekW2SO6Qb+cIw5lVdp+H5Wy/BD2+YiwktMVzVdZrtsGot6sJ6g70HCQkphQKCQoFBW3O2A4PRvpUKUI36Cz5/6yX48k9f9bTnoBNoSmHPoUH09O7EpPFx3HrZzCKRxcqFnbj36V04ciyF7147B5pCkXowmcogo2n4gh4O3Lzi44b3oWfBLMy771f517n7omkKw2k9pLihsO9gF9p0D9HuKsh13puQvQcJqWcKBQbLN5RvxmpFUg0YhxFznsiyknME/Sk/OZLJ56lyjWxzHSuOj2RwxxOv4sixFNZ2n19kQMa3nDD4X/jBy3kjtfqZ3Vi1qLMot5czfDlyIdfBVBqJpmhRSBE4kUt7aOkcwy765YxSI+WtytG4V05IHXA8rRkIDIwFFlYmPaOan0PvpdAWj1U0eG5Qi3eRaIoWSeR7XzmA3lcO4KLpE7FuyWys/mxX2WOW5sF6XzmAiAAPLZmDRDyK4ZEM/nR8FN/8bBdu/kRHfsmRPYcGsem1g7j2I2djXItJaLUkl5jJaDiaHKn48OHEfQk7NFqEhBinuyGY1fwUeiJePeVX21OvcEJvjgrWdHdh45a9RSHURFM0L+QwI9cFpNSAAwAUMDSSxoofv1KkJIxHI/j5qwdx1fln4KZHt+Gez3RaKmweGslYfvhotF6DpVCIQUiIcVpVFqSan2p66pUKSa57eCsSTVGDGq3K/RIjEWDVomJhxqpFnYhEcmMbqyQcTmu46P2n4PbHd2DS+DhaYpExx1jb3VUUWk2OZnNoVh8+Gq3XYCn0tAgJMUae0ZrurhOd0KsIGwUld1KNF2lUj/bO4EiRgMJqjVpLLIrx8Rjuvvo8TG1PYN9AEuN1AQXEuA7rtJNa8z//fNnHsGxjHyaNj+dzafsGkmgrKRNINEex59Cg5U4jhUut5DrKf/v5/rrtNVgKjRYhIaZUYDE4nMbDv33TtFWQEUHNj1TTU8/I0E1tT1QVQo1EBONbmhCNRiACnDI+nr83Zu2k9g0kkUpr+NBZ7UUikFwbqFhEsPuu+WOuMye3L132pSU29uFjeKT6pVbqAYYHCQk5Oc8oOZLBTY9uw+rNe6oOp5m1d9I0heRIGoPD6XwLIystoGpZnLCa2iSjcOm+AeMaLSsh1NJC5pzhMGsn9WcT4jj9pBb88Ia5ODY8imWXdlQ8b6Ipiu65Z2LngT+OWfblwB+H8b1fv5H/TjRNIW1Q7G1lqZV6gXVahNQJmlI456tP5uXdwIkn+4gYe05GdVmlNViapnBseBTHUumip/u13V2Y2BY39crsKuIMr8mmF2gkUnjgmgswktHGSPZr9UBL32+NRTCQHC0O1VZoklt4rKGRNG5cv82wDqyndyceWjoHANDaFMXMO+x9zyGBdVqENBJOhdNKQ2fJ0QzeTY4a5IWy9Ua5/n6FkzsAW4o4M+zm18zq0QDYkuxbUegZdaAvzact39iHdUtm4+8+OaPseSMRMRVj5MKMue/ENP+VymBcS/1P6QwPElInOBVOKw1hJZqjZfNCRuHF4bQ9RZyTGIX0zMJ8ZlSj0DN7AGizeN5y/Qpz30lh/qvwe16zuAsZTXNlBemgQaNFSJ1QTq5ulluyYuiSIxnTvNBQgXdROLlrGso2oA06VSkXbfR3tPpdrFzYiU2vHcx/J7n81xPb96NnwSzsunM+HrhmNn7x6kF84QcvN4TsnTktQuqcSqGuSrmbTEbD0Eg29LT3aBL3bd6dX4qjva0ZM+94yjC/8q1f7hmzqOKa7i6cUiYP5iVGOancysJDqTS+/5s3sXrznvz+lfotWgkp2vkuhlJpJJqjY5Z/yYpiMmhtjqL/8CDuf64fva8cqIe8FnNahJDKa2lVWjG3VFywtrsLbc0xtOi5LLP8SvfcM7Fhy9v5GqVcM9+gGKxC47Hs0g4svnAalm/sKxJRACgSUZQLtVrp72jnuxjf0gQAGBePjDkPBLjmO1ss5y+DWtZQDQwPElKHFIagoIBTJ8SL3reaWzLO7fRBQ3byNA0vNkcxsa0Z139sOmacOg7HR7OTaWn3+Vqvza6M3uy65p07xXCl4es+enZRqBVA2XNXyp2VCzvauS47+ctaVq0OIvS0CAkRVp6YjUJQqxZ1QlPIF7lWUhXmsLJibjnvwunOGk713Su9LrNViwtFFE6c20zhmV0VOWP52Fa79gO1rVodROhpERISrD4xG3lHtz22Ays+fY7tBQStiAvsKvNqwam+e6XXZUU04sS5zTwkTYPtY1u97043VfYbGi1CQoLVSdNskpo2MWG7Ca6VMJQT4TqruBVe2/TaQaxZ3FX2Op2Y/M0UnqXLoFg5ttXrdbqpst+EzzckpEGxOmlWKjK2ExKqFIbyepkM02tLZXB8tLbwWmssUl5EUUXxthFGwhezXoblhBWlq1YPmqxabbbcTNAX8TSDnhYhHlOtZ2L1ibmaIuNylAtDeb1Mhtm1RSK1h9ei0Uh5EYXD97WWYydHM9iw5W1cdf4ZRb0KjybHhouDtNyME7BOixAPqcUzsfNZpyXOZserpt9hNeeptA8Ejo6jlvF5cWxNKew5NIie3p1l+0aGDEs3kp4WIR5Si2di54nZSXFEJqPhnaGUoQDEyXyJVaGJ0bXVMg47nq/ZffUyrwdkvW4zxWNYBRZWodEixENqTeZ7qdQDxi4FX2ponQyZ1WLQqx2HEzVMXhyj0CgeGx5Fa1MEyZF0XQksrMLwICEeYrYUyLols9HW7L4RsstgKl1xKQynQma1hhqrGYeVpVkq4fYxEk3RMWHhlQs7sfPAHzH7zPaiLh7lQs0h6IrB8CAhQaM1FhkjrV6zuAu/2XMkkF0KEnp/u3JP9E55f7WGGqsZhxMy9rIyfIvhwnLHMPJAb398B6ZPGo+NL+3FuiWzK4aL66krBo0WIR5yPK1h40t78x26exbMwsaX9mL6pPGuqu7MqJSLMV0Ko7vLccl0raHGavJKTuTkzI6x92jSsoEoNw4zg9YxeRzWPttvaekTr1WebsLwICEeYhYC23XnfMy840lPu3Tb6UpeWA80ZFIP5NSYqglhVavKdKLOzKxt1j1P7cq3zaqlQ3xyNGMcOlwyB+8MpjB5QhyJ5rGNjovuY3PUE3VljVgaCI0WIR5ilrsoXFLdK7my1VxMCHIhNeWVnLi+omOkMrjjiVfxRN+B/PtWDIRpWYGJUbx30678EjETC5Z7Mdr/wc/Nxk2Pbgu6PJ45LUKChpWF/jwbi8V8jteKxWqoJTflxPUVHgMCHHovVfS+lZCj2ThKSx3uvvo83PPULjzRdyDfdb8wzGcUCnz4t29iTXf5NlVhITAmlpB6w+zJubB9UG6hv+s/Nt1zD8aptkRBIEjX4kbbpJxB05TCp1b/e1GYr9Q4Gxnwtc/24+ZLOyx1hQ869LQIsYidRH85tVbhE/X4liZEIxHLT/i5MWQ0DceGR4vGYleIYFX44FbhrJPHdbPFkl3cbJtkRThits/xUS3wHrMVmNMixAJ2E/ZO1O6YjSHXc65wGfu13V1ojkbwhR+8bFuIUC6fU+66AVSdC3Kj0W4Ycm+1Ykc841UTYwehEIMQp7BrhJzuyVc4hpxoo3Qsd199Hi6593lL47N7TiPlmp2u6paPGyxhAIDgGcNq+zMG3GABFGIQ4hx2E/1urGGUG4NZz7mp7QnL47N7zjHHjUdrqvsJy8KERmHed4ZSyGQ038ZkRTgSBvFMtdBoEWIBu0bIjRxLbgxmHSr2DSQtj8/uOcccN5Upa3SsFC271TfPyVyZkRJv+YY+DI1kQtlNoh6g0SLEAnZFCxCgtSmK717rXDI+NwajDhVru7twcqLJ8W4S5davMjM6VloGuSWccLpdkZlH2BaPhbKbRD3AnBYhFqlFtOD0mkvZLt+Z7OSZW1MK1QkjKo3bcP0qwH4HBw+Klp3OlZUrBp9x6jjX1gyrZf8QQyEGIV4SJnFBIdWOO0gLQ+b3deHc7wylsHzDiU7qKxd24ont+3H9x6bb/l7tPtiEWAlYDRRiEGIVJ/IgYREXlFLtuM2S/W4vDHlseBSDw8bfldO5skhEMDHRjAc/Nzvf4PiJ7fvRPfdMT9YMq6dGt05Bo0UaHqfyIMmUmWgh7eRwHcfpid7NhSEnjY/jWCqNG9Ybf1du5Mqi0Wzx9/HRDGacOg7Xf2y6oadj5cHH7gNCWB+E3IRGizQ8TjzNappCRIC1Jf3dVi3qRFrvVuEUTneocHqid7IjROmkffMnOnDbYztMvyu3ulFUkpBbffCx+4DgpsoyrLhutEQkKiLbReTf9Ndni8gWEdkjIj8SkWZ9e1x/3a+/f1bBMb6sb98lIvPcHjNpLGp9ms1NWJ9/ZCvu+vnruPvq87D7rvm45zPZ5Sm++IOXHQvnuLGYnxsTvVsLQ5rVqPnteVh98LH7gBCk9lRBwYvs8HIArwOYoL9eCeCbSqmNIvIAgM8D+Lb+/7tKqQ4RWazv91kR+QCAxQBmATgNwGYROUcp1biPGsRRam22WjhhAcATfQfyCrPeVw4gFhHHJtXSc+Umx1rFHjkjAyBQopHS5rP7BpJlvyu/hAt2OuYXNkyuJCyxu38j4KqnJSJnAPhLAN/RXwuASwH8RN/lEQBX6T9fqb+G/v4n9f2vBLBRKZVSSr0JoB/AhW6OmzQWtT7NlltZFshOqkMONZpttBxHqRc4eUJ8TAi28LvyS7hgJ4xn1wuNRASJphOLOSZHM8hkNFeaGIcBtx+p7gPwDwDG668nAvijUiqXmd4P4HT959MB7AMApVRaRP6k7386gBcLjln4GUJqptanWTNPrf/wYH69rO//5k10zz2z5if+IC3BUS12644KvcBEcwwtsajpd+WXUXdjOZIcRt7jmsVd2PjSXqx9tr/eZfBjcM3TEpH/BuCwUmpb4WaDXVWF98p9pvB8N4rIVhHZeuTIEdvjJeHEKVFCLTkYI09tTXcX3j+pDT0LZuHep3dh9eY9jjzxhz3HUSknZ+X7LPdd+SVccHU5EqNWUhv7MO/cKQ0pg3fz0ewjABaIyBUAWpDNad0H4CQRiene1hkAcmtS7wcwFcB+EYkBeB+AgYLtOQo/k0cptQ7AOiBbXOzKFZFA4dayGXYV/Df1AAAgAElEQVQx8tRamyKYecdTZRfrc+pcYcpxlMvJJZqiNeej3PR4KuFWXrBS+Dn3ul5DxKW45mkppb6slDpDKXUWskKKZ5VSfwPgOQCf0XdbCuBn+s+9+mvo7z+rsu06egEs1tWFZwOYAeAlt8ZNwoNp/mIk47jCrhKlT//HRzXXnvjD3MG7XPjOiXxUJCJoTzRh3ZLZ2H3XfKxbMhvtiaZQ3aNSzLzH/sODRa+t/G65taCnl/hRp3U7gBUi0o9szuq7+vbvApiob18B4EsAoJTaCeDHAH4P4CkAN1M5SAD3ls1wZGw+h/GCOjmVC985kY/SNIWB5ChuXL8N53z1Sdy4fhsGkqOBuf5qMAw/L+7CptcO2vrdcqNcwg/Ye5CElnILFHZ9/WlHF2CsBr8anQa5X125sVlttJs7jtG99bv/o1vfeelxW2MRHE9rts7j972xAHsPkvqmmmUzvMSvMJ6VMJtfnlg5wYKd5V/MPAY/SwLc9GRKf5dyraXs/G7VS7kEjRYJLWYTYEss3Aq7Wqk0ObkxudoxgmbG3KoCr5xR9rPtUbU5Oa8eIKq5N0EMM9NokVBjNAG6KT8OA5UmJ6cLcJ00gla803JG2c9cYjWejJd5Jrv3Jqg5MBotUpe4HZoL4hNojkqTk9NhIq+7UJQzyk49sFTz/VbjyXh57+zem6Aui0KjRYhNgvoEmqPS5OT4UiQe50oqGeVaH1iq/X6z4yptMdVV1svz+t7ZuTdBzYEFQjJCSJhwq2mtk5QrdHW6ANfr1lJuF1jX8v02RyO4++rzMLU9gX0DSTRHy/sFyZEMll3agXnnTkHH5HHoPzyITa8d9KQtVyWlY1BbhlHyTohNnF7S3Q+clGYHQWLv6PVU+f1WIynPZDQcHRrB8o19RX0FJ7Y1I1rB4NWCle/Mh+/V0kGD8VhIiAu4VTPj19Oxk9fjZMshv1tLOT25VuthVBNOO57WsHxjX5FXt3xjX9bQuWi0rHiTfn+vZjCnRUJNLmGe0TQcGx7NJ85zT7Bu5J1aYxEsvnAaenp3YuYdT6KndycWXzgNrTH3n4yDnEfzq7WU04KBahWI1eQKfetKb2P9r6C1DKPRIqElN5F/79dv4D/fHc637rnhka04mhzBhi1vu6J8Knw6Luy6fTytWR53OWWa0ftBVXIFAacn/moViNUYO7/qyvysZ6sVGi0SWnIT+bxzp+D2x3cUG5EN2aUbCnHqCbaWSdLK0hxG77c2RQKp5AoCbkzA1XgY1Rg7v+rK/O6NWQs0WiS05IxHx+RxFZduAJx7kqxlkqzkMZXrXB/WJ2O3CdIEXM2qxH4Uwoe5AJ9CDBJachN5/+FBw8T5UCqN52+9JC8/PmVcM6Cy6rBaksq1SMYreWlm77fFY76tE+U0TgtkgioYsIpb63AF9by1Ep6RElJCznhs2PI2Vi7sxO2P7yiY0LuglMKXf/qq6RLlD1xzAaKRCBJxexNdLZNkJWVaufeDPDFbNURuyajDOgET+7BOi4Sa3GTZ2hRBciSDNn3ijwjw+YfH1sz0LJiFeff9Cgs+eBr+4fKZuO2xHZ7WFlWatINQ82QX4zF3oS0eQ0tTsJYOIYHG0i84jRYJLEVP76kMIhGMmQRNPzeSQWtzFP2HB3H/c/3ofeUAYhHBrjvn4/1f+QU23XIxenp3+jJ5VvJK/FqHq1rMDNHdV5+HT63+9yLDC0HoC7OJa7C4mIQXo6f3VYs6ce+mXTj0XsrU+zD63MqFnQCAI8dS+SXKzcQbXqjxKoWywhbqMsvDTW1PFIlJHlo6BwAC2RqInCDoD01UD5JAYqSiu+2xHfjiJR1la5SMPnf74zuw4tPnFC1Rvm8gaUuNF+Su7n5jpmzMPSAAwVg6hFQm6EXsAI0WCShmT+85GbuZV2T2uWkTE5jY1ozrPzYdu++aj8kT4gYduY0nT6M/5HeGUshoGg0YjCXnqxZ14v7n+vP7OL10SFgJ+sNPGIrY6Y+TQGKmoss9vZuFlCqp83L93BLNMbTEopbUeEZ92pZv6EPPglno6d0ZeKGE24xRU6YyyGgajhxLIRaRMfJ8v8Kffoe9wiCyCepyJIXQ0yKBxOzp/dvP95f1iuyEn6wWgpbz+oL4JOoHRfeyJYbxLU2B8qZqCXs55R2FwYsJQxE7PS0SGIqehEczaE80FT29RyLA6s92lX1KdqPQtJLX58eTqN9eQyWCJiapdo0sJ72jMHgxTq+15gb0tEggMHoSHkiOZidj/ek90WytPY7TnamNvLeVC0/kbLx+Eg1DsjxoVGswnPSOwuDFhCHnSKNFAoEboROnwjqlf8gPfm42nti+H7949aAv6rcwhJmCRrUGw0nvKCzKySAuR1KI/347IXA+dOJ00rs03HX9x6bj7z45w5fQXBjCTEGj2rCXk0vOh71HYlCgp0UCgdOhEze9Eb+fRMMQZgoaXq6RVWkcQfZiwgDbOJFA4LRnpClVt+2C3JBOB13Y4Se8N57BNk4kPDgdOnEyrBM0nL5XYagf8pOgKSEbHYYHie/kBBP55yyFmkMnYUl6V4uTYSajUOqGLW9jaCS4nRtI48LHBuIrmYyGo8kRLN/Q5/j6Skx6W6NU2LHgg6fhqvPPwI3rt9HzImPwO1xKT4v4hqYpDI1ksHxDnytSdyc9t3qmVNhx8yc6cPvjOyipJ2MIQo0gjRbxjeRodtFGN6Tu9VR463aT1dJQqp/LtvjdUNbv8wedINQI0mgR30joizQ6Id/OTTbJEf//qJzECyNcKgdPjqR9kdT7/cDh9/nDQBBqBGm0iOfkw3cATko04d5FHywSTKzp7rIlmCicbFoD8EflJF492RYKO9qaY76IWPx+ivf7/GEgCDWCFGKQmrGTmDWSV/+vv+7CPZ/pxGkntWIolUZbs73EbuFkk/Pc6kXq7seTrV8iFr+f4v0+fxgIQkNdS56WiCy3so00HnZDKkZPs3//L304OdGM46NZwxKN2gsAFE429z/Xj5ULO+tG6u7Xk60fnRv8for3+/xhIBIRtCeasG7JbOy+az7WLZmN9kRTINWDSw22XevgOEhIsRtSMX2ajUernhwLJ5veVw7g3qd34e6rzwtsl2o7VKo3qyfhgN+1dX6fPwxomsJAchQ3rt+Gc776JG5cvw0DyVFPf+/KxktEpBvAXwM4W0R6C94aD+Co8adII2E3pOJGp4rSkMWRYym0xWN5qbtT+FGfUi5UV2+dLPyurfP7/GGg2nXJnKTSWf4PgIMATgHwzwXbjwHY4dagSHiwa4TciIl7Mdn4aSDM2gj5NYG4abz9bpnk9/mDThDyfmW/FaXU2wDeBnCRN8MhYcOuEXLLwLg92QThCbMUPyaQevPuiD2C0NPTqhDjahHZIyJ/EpH3ROSYiLzn9uBI8KlmyYcwLs8QhCfMUmoVDlSTD6MsvLEJQt7Pqmm8B8B/V0q97uZgSDipxcvxu4+ZVYLwhFlKLaHWaj2mIBpv4h1ByPtZVQ8eosEiThOmDgRBeMIspdqFDYHqPSbKwonfkRJLi0CKyBoAfwbgCQCp3Hal1E/dG1r1cBHIcDCYSuOGR7YWeS8XTZ/oa56oHGHxCq1Q7SKZzGkRF3F0EcgJAJIALivYpgAE0miRcBC2UJPbYg8vjWK14c4ghIdIY2M1PBgB8P8opa5TSl0HYIWLYyINAkNNJzAKlR4bHsXgsDWhhF1RRS3hTr/DQ6Sxsfq42KmU+mPuhVLqXRE536UxkQYhCH3MgkKppH7S+DiOpdK47bEdFcNw1YTs/PCY6im8SvzDak7rFQCXKKXe1V+3A/h3pdR5Lo+vKpjTCg+cyLKU5pg23XIxenp3Wsr3hSE3yFwYsYClXwSr4cF/BvB/ROQbIvJ1ZDtl3FPtyAjJEZZQk5M9/oyOVRoqNVuIsbUpMuazYcgNsr6LOIUlo6WUWg9gIYBDAI4AuFop9aibAyPEDaoxPk5K882O1RqLFOWY9g0kx+T7ll3aYfjZ4dHg5wbDYFhJOLC8BoRS6vdKqW8ppf6XUur3bg6KEDeo1vg46SWYHet4WiuquZo8IY613V1FQolrP3I2lm/oG/NZTUPgashKoeiGOEUwAt6EeEC1/QOd9BLKHSsXIs3uF0NLLFoslCizrEuiORpoGTpFN8Qp7K22ZwMRaRGRl0TkFRHZKSL/U99+tohs0XsZ/khEmvXtcf11v/7+WQXH+rK+fZeIzHNrzKS+qdb4OOkl2DlWab6v3GeDnhuspXsHIYW4ZrSQ7ZxxqVLqgwC6AFwuIh8GsBLAN5VSMwC8C+Dz+v6fB/CuUqoDwDf1/SAiHwCwGMAsAJcD+N8iwsczYjs/Va3xcbKFUy3HCmIrKTsE3bCScGBJ8l7zSUQSAH4D4IsAfg7gz5RSaRG5CECPUmqeiGzSf35BRGIA/gvAJABfAgCl1N36sfL7mZ2Pkvf6pxoJdS2yayel+bUciyUCpI5xtI1TdSPIekTbAHQAuB/AHwD8USmV1nfZD+B0/efTAewDAN2g/QnARH37iwWHLfwMaVCqyU/VUlDrZAunWo7FRQpJo+NmeBBKqYxSqgvAGQAuBPAXRrvp/xvNHKrM9iJE5EYR2SoiW48cOVLtkElIqDY/xRAVIeHGVaOVQ28B9TyADwM4SQ//AVljdkD/eT+AqQCgv/8+AAOF2w0+U3iOdUqpOUqpOZMmTXLjMkiAcFpCbTU/5mSRcZBplOsk4cNN9eAkETlJ/7kVwKcAvA7gOQCf0XdbCuBn+s+9+mvo7z+rsgm3XgCLdXXh2QBmAHjJrXGTcOCkKMFq/VYQ1v/ywpj4cZ00ksQqrgkxRKQTwCMAosgaxx8rpb4uItMBbATQDmA7gGuUUikRaQHwKIDzkfWwFiul3tCP9VUA1wNIA7hFKfVkuXNTiNEYOCVKsNq7z+kef3bH71X/Pq97GbIvIdGx9GV7oh70GhotYgerCyJWu3Ci4TmrmKi9MiZOXqcVwtDwl3iCow1zCalbrObHHC0yrqI1lFf9+7xuucS+hMQONFqk4bGaH3O0yLiKidorY+J1ETP7EhI7MDxICKznl7zOo5We26vcj5dFzMxpER3mtAhxm2on92on6nrtiFGv10VsQaNFiFMYTaoAavIQOFETUgSFGIQ4gVnd0nC6tnW22J2DEPvQaBFSATOln6bBVdUbC24JGQuNFrFELRNo2CffcosvWlW92b0HQei+QUgQodEiFallAq2HyddUkp3KWJKGV3MPqqnjIqQRoBCDVKSWjgX10O2gnNIPQEUxRVXydo+7UtiBAhLiEv6vp0Xqg1o6FtRDt4NK63BVWt/K7j3QNIWhkTQ+dFZ7kaHLhR79NPasqSJ+w/AgqUgtHQvqpdtBLUo/O/cgZxS+/5s3sXJhp2ddKazCsCXxGxotUpFa2vp43RIoiJjdg9ZYZIw4I2cUVm/eg3uf3oWeBbOw6875WLdktqk346XQpR48ZxJumNMilqglj8EcyNh70BqLYCA5OibM1t7WhJl3PGU5l+V1uK4ecpQksLC4mDhHLeExFtGO5fioZhxmsxlO9TpcR8+Z+A0fjQhxGSNv6Ic3zDUMs7XFY1jbff4Yz8nMKHgdrqskSiHEbWi0CHGZQm8IAF544yj2Hk2aqgOtGIXCcOPmFR/H6md2o/eVA0XHcStcl/OcAXPFJCFuwd84QlzGyBu6b/NurO3uwrINfWM8qkpGwchzW7WoExEBDr2XYriO1DU0WoS4TC5PVehVHXovhbZ4zNCjqiRcMfLcbntsBx5aMgcQMFxH6hoKMQhxGTPxQkssOkagYqXlU7leiBS6kHqHnhYhLmNHvGDkRS3bsL1IUm7kuQWhWwYhXkBPixAPsCr7t6IGpOycNDJ8LCOew2Jjc6x4UZSdk0aGnhbxlHpYqsQOdlosaZpCRIC13V0VvSgWbJNGhZ4W8RQrORsn8dOrs9NiqXDfUyfEcffV52HaxASSqezYaZQIyUJPi3iKlx0cjLy6Y8OjGBz2prmsnRZLhfs+0XcAl9z7PP7moS2AgAaLkAJotEjN2AmB1bpUia1zlRiNSePjOJZK44b13oQm7Rhodk8nxBo0WqQm7HoztSjf7ObDSg3BzZ/owG2P7ai5uaxVw5lMmRjolIGnFbJ1xzRNnfiOh9NIjrjrtRKSg0aL1IRdb6ZQ+bb7rvl4aOkcy8to2O1oXmoIOiaPq9mbsWM4IxFg1aLihRxXLepExOCvLkwy9uw9SJ34jtdvxcDQCI4Nj7pmuLxcM4wEGxotUhPVeDPVKt/shtBKDcG+gaShNzM4bH0StGM4W5qiuHfTiYUcexbMwr2bdqHFwBDVYsy9JnsP+oruwW2P7cC7yVFXlkRpNMUpKQ+NFqkJN7wZq+cCyofQSg3B5AlxrCmRk69c2ImHf/um5cnWjuFMjmRw6L0U5t33K7z/K7/AvPt+hUPvpcqONwwydrN7MLU94UoOzus1w0iwodEiNWHVm3EiN1NNCK3QECSaY5jY1lzs+Ty9C2uf7bc82doxnGEK+dnB7B7sG0i6koOjSIUUIkrVn4s9Z84ctXXrVr+H0TAU1kINj2YwlEqPWXLDqVBXrXVXtS4Xb3d5+3rs/pHLaRV+x6sWdWJ8PIbxLU2OX1+t3xkJDZZ+cWi0iOMEeaK2a3TMjhHU6/MKTVPZa49HkUxlEIkALTF37oMT3xkJBTRapH5w0lDQ6IQPfmcNgaUvlL41CTxOP2lzufjwwe+M5KAQgwQeqscIITlotEjgqaQeY+EpIY0DjRYJPOVk5iw8JaSxoNEigceo3mlNdxdaYxGGDglpMJjRJJ5SjQosEhG0J5rw4Odmoy0eQ//hQWzcshfdc8/ExHHNrheeUrlGSHCg0SKeUYsK8Hhaw02PbisqMH3hjQGsWzK74vL01YzTq2JpQog9GB4knlFLKK+cGMPJVkmlObLD76XGNIe1G36kUIQQ56CnRTyjlh5yOTFGqUd1fFTLN8V1InxXaFgBYGp7oqbwI7s5EOIs9LSIZ1Sz0GHOS2ltiozp0J7zqMy6oxd6OMeGR5HRtIqeTqlh7T88WFMDYApFCHEWGi3iGXa7nheG6mbe8RQ2btmLBz8323C9qdIQXCajFYX5bly/Df/57jC+9+s3cHRoBJmMZhiyKzWs9z/XP2YhRzvhR3YoJ8RZ2HuQeIodJZ7V7t5GIbgHPzd7jHDjoukT0bNgFja9dhCL507DcgNxBYAxx3rgmgsQjUSyzWFthh/ZoZwQy7D3IAkednrIWfVSSvNQL7xxFG3xmOFnOyaPA86dguW6uCK3/7IN2/OGpFyOzK6hyXmXpTmtsK+pRYhf0GiRwGImviiVsxsZt1wuqvSz/YcHK66uXMmw2vEWC1dPNtqfNWCE2IM5LRJYrObAkiMZLLu0A5tuuRh/+McrsOmWi/HGkWNjhBsrF3Zi02sHMZRKVy2uqKZtVDmhCFtQEWIP5rRIoLHiieREF8s3nshRrenuwsS25uxncp9tjuL4qIbWWAQDydGqZOhO5qj8zHfRwyMBhDktEjzsTpZWcmDH0xqWbyzOUS3f0IeeBbPQ07uzyCCNi2eDC9XWdjmpBvRLWcjaMRJmXAsPishUEXlORF4XkZ0islzf3i4iz4jIHv3/k/XtIiJrRaRfRHaIyAUFx1qq779HRJa6NWbiLm6Fw8wm/47J40zrosxCdpWoptbMi2PZOi9rx0iIcTOnlQbwP5RSfwHgwwBuFpEPAPgSgF8qpWYA+KX+GgDmA5ih/7sRwLeBrJED8DUAcwFcCOBrOUNHvKXWdkRuTZZmk3//4UEAznovdmvNvDqWrfOGoHaMra+IGa6FB5VSBwEc1H8+JiKvAzgdwJUALtF3ewTA8wBu17evV9kk24sicpKITNH3fUYpNQAAIvIMgMsBbHBr7GQsToSUrCzmWE2epTWW7ZZRWHe1cmEn7n16F4DaG+gWUkkN6Nex7GBVlekXDF+ScniiHhSRswCcD2ALgFN1g5YzbJP13U4HsK/gY/v1bWbbS89xo4hsFZGtR44ccfoSGh4nvCQ3FnPUNIWB5Cg2btmLngWzsPvO+Xjwc7PxxPb9+MWrB13xXqoNLbp9LKv45eFZheFLUg7XH6tEZByAxwHcopR6T8T0j9LoDVVme/EGpdYBWAdk1YPVjZaY4URIycgjyk2WRgXChQW/ZhR+bvXmPQCAFZ+ages+ejb+7pMzqIwzwC8PzyphCF8S/3DVaIlIE7IG64dKqZ/qmw+JyBSl1EE9/HdY374fwNSCj58B4IC+/ZKS7c+7OW4yluHRDDav+DimtifQf3gQ9z/XjyPHUpZDSlmPaCTvEXVMHoehVBptzdnJstqJyuhza5/tx999ckbeeyFjsdOZxGuCHr4k/uKmelAAfBfA60qp1QVv9QLIKQCXAvhZwfYluorwwwD+pIcPNwG4TERO1gUYl+nbiEdomsJQKo0v//RVzLzjSfT07sQ/XD4TD1xzgeWQUnIkg2Ub+rB68x7Mu+9XeP9XfoGbHt2G46Na/v1qlHR+KfDsQmGBdYIeviT+4lpxsYh8FMCvAbwKQNM3fwXZvNaPAUwDsBfAIqXUgG7kvoWsyCIJ4Dql1Fb9WNfrnwWAu5RS3y93bhYXO4tpEeySORjXYu3JV1MK53z1SaQLJutYRLD7rvmIiJRNvgMwFWiEIWkfhjEGDRY/NySWvmB2xCAVqWRwrDA4nMb3fvMG5p07BR2Tx6H/8CA2vXYQ1390et7wGU1UwNiu66UTftAnOHZ6J8QSlv5o2XuQVMSJEFwsAiy+cBp6enfmQ4yLL5yGWAT5UFkkIllRRq710mgGw+nKSjI/FHh2oLCAEOeg0SIVcSLHkNaQb7WUMz7LN/bhv95L5aXtRrL3oVQap06IFx0rbBN+WPJu9QBzh/UPjRapSKFE2mjVYCsk4sbextT2RN5zMq7P6cMtnzqn6HNhm/CdEhZwQi4Pu+Y3BgyoE0vUKpE2kzH3Hx4s8pyMDNu0iQlcNH1iQU6rK1RKMifqoijmqEy1tX4kXNDTIp5g5G2sXNiJ+5/rz3pOqQySKeMw2uBwGndffR523Tkfd199Hpqjjfdryy4RlWHusDHg4wfxhJy3sW7JbLTFY9h7NInVz+zCkWMprFrUiYhuh1Yt6sRtj+04sS7W4i48/Ns3890ugPAp77zo20hYlNwoNN4jK/GNSETQFo9hxY/6kEpr+Oe/6kLPglm4d9MutDRF0dIUxb2bdqFnwSzsunM+ehbMQntbM9Y+2190nLBN1m73bSRZWJTcGPDxowHxs64pOZLBofdSmHffr/LbLpo+MT/5lr73/K2XhP7p2QkvKTchl3prnJBPEPSeisQZ6Gk1GH4rrMo9DRu9d3KiCWu7u0L99OyEl+SEgrMRCHrNHqkddsRoMKx2Z3DTGys89lAqjURzFMdHtbwhMuqKEeSOF5Wg8o8QS1j6YwhHfIU4hlGo6tQJcUBl2zUlRzJojUUwkByteZI1M3yRiKA1FsHgcBpt8Vi+pVP33DMxsa3ZUFof1I7kVmDYihDnYHiwwSgNVS344Gm4dd5M3LC+IFyYHMGGLW/XJBwoF4bUNIWjyRHc9Oi2fEunq84/Axu2vO2ahNvvwlyGrQhxBhqtBqM0b7Ti0+fgtsd2FLdX2tCHeedOKfqcXeFAOcVccjSD5RuKWzrd/vgOzDt3iiuqQL/zeIQQ56DRajBKE/rTJiYMlW0dk8cVbbMrHCinmDN7r2PyuKJzOOUdsTCXkPqBRqsBKQxVmSnbhlLpmhR75RRz5c6ZaIpmjdVwGhDgnWMprPhRX03ekVeFuX6HIAlpBGi0AowXk6CxBL0LzVHBQ0tqaJBrU9q+prsLbc0n1s/K5di+/NNXseLTMzFpfLxq78iLwlwrIUgaNUJqh5L3gOKlTFrTsqrBRDyKvUeTuG/zbhx6L2V4PjtS+HL7mr1nJsnvWTALf7n214YLT1Yakxf3slIpAWXvhFSEkvcw42XH6khEAAH+5qEtRZNu6fnsTrzlOsObvVcu32XUCcPKmLyQnFcKQbIDOSHOwPBgQPGyQaqmKUvn80LQYBbK2zeQNMyrWR2T25LzSiFINrwlxBlotAKKUT3V5hUfBwBb+ZDSPEomo415fXRoBHuPJivmfbyYeM1ybJMnxA09uqAYg0rNWtnwlhBnYE4roBSGvU6dEMet82YWLdlhJR9SGjpbdmkHFl84Dcs39p1Y+qO7Cxu37EX/kSHcetlM3P648Tk0TWFoJI1Ec7aDxf3P9aP3lQOWlwlxIhdmtD05mrHUlsoLKuXwmNMipCyW/hBotAJMbhKEAm5Yb39iLhUHbLrlYvT07hxznG9fcwHGtzThwB+PIyLAn72vFcdHspNvzliUTrgrF3biie37862X7BjPaiZss2O0J5ocaTnlBX521yckBFj6Y2B4MMDk8jCJeHUhsFzobMEHT8OmWy7GjFPHGR5nfEsTZt7xJP7hJzuQ0YD7n90DCPITqlHe6PbHd+C6j55tyTg4sp6UyTGOpzXXu587JVVnKydCaodGKwRUmw9JjmSw7NIO3HrZTPT07sSeQ4OGx+k/PFhkjK79yNlFgodS4/eHf7wCPQtm5T2xSjiynlSZY1QyBrUYHbaAIiRY0GiFgGpXZE00RXHtR87G7Y9newve/1w/Vi7sLDrOyoWduP+5EysD/+6tAYxrKZ74S41frsmt1cnbCRFCtceoxugUGrmhkTRbQBESIJjTCgnV5kM0pXDOV5/EFedNwc2f6EDH5HE4NjyK8S0xJEcy+P5v3sTqzXvy+5utrTWYSuOmR7dVJXhwM6dV6RhW1w8zO8+uO+dj5h1PIl1g5GIRMSxyNjoWc1iEWIbFxfVEuULdcuS8pKvOP6NIGbimuwsTE83onnsmXnhjoOwS7pGIYFxLrOoQnxPFvdUew25osrQIuP9wNqRaaDpY+UQAAA6USURBVPSMipxLoVqQEHdgeNBFvOo1V3ieY8OjyGha/nylIcLC5UfsiBhqDfE5IUKo5hh2x11q5IxCqlZCs+wsT4g70Gi5hFcJ/NLz3Lh+G/7z3WF879dv4OjQCACU9ZKsGoJq82p+Y3fcpUau95UDeGL7fqxbMtuWOjEoRc+E1Bs0Wi7h1ZO2mRx93rlTTiy66IAQonQdLjek5W5gd9xGRq577ploa3bXwyOEWIM5LZfw6km7XIPZ/PkUsLb7/JL8ShcikhVqWM0PVZtX8xs743aquW7O+JXmtILumRISdMIz84SM3JO23QS+U+fJCQhy5yuaiFMZZDQNn394a5ERa4vH0BKjws0J4+xFZ3lCGhGGB13CqxyQ0XlWLuzEptcOFp2vMHcFAb7wg5dLQpd9OPxeioWzDsIOGIQ4D+u0XMStOp3S47bGIjie1pBojmaXrG+O4vioZnq+XO1Wae3Rrjvn45rvbAn1Gk+sjSIktLD3oN+48aRtpEocSI5mJ2cRjG9pQjQSKXs+M5FA/+HBUCvc2HKJkPqHRitkOKFKNAsp3v9cf6gVbqyNIqT+CWcMqIGxokqsFCLLiwSWzEEiHsXeo0msfmYXjhxLhVrhxtooQuofeloho1L9j9UQWa41ExRwyvg4Vn+2y3btlVcdP6zC2ihC6h8arZBRcVl3myGyavNuQcwfhbVrByHEOlQPhpCyy7qbKAOtdCW3g93u6V5B9SAhoYVd3uuVcsWvXhU1tzZF0LNgFjomj0P/4UHc/1w/fvHqQd/zR2Ht2kEIsQb/qusML9oH5UKDPb078+dYubATHZPaHDeOhBBSCMODdYimKQynM9A0IBHPtm3KdXSv9DkroTWz0OCDn5sdiM4PDBESEkoYHmxkhlIZWwsQ2lm00ExaPq4l5mjerBq4+CIh9Q3Vg3VINUW2dj4TZGk5C4wJqW9otOqQaops7XwmyNJyFhgTpwhaHSLJQqNVh9jxhHJ/mACwecXHseCDpxV9Zng0M+YP188FIStNJEH2Akl4CGIdIslCIUYdYjWvY7TfqkWduHfTLhx6L4UHrrkAIxkNyzb0BSI/ZOW6mNMiThDUOsQ6x9IfKI1WnWJFQWf6h7lkTvbXRwE3rA/OH67ViYTqQVIrXhXpkyK4NEkjY6U9k2n+Jx7FuHgMiXiw8kNW81VcfJHUCsPMwYVGq4Gp9IcZtD/coI2H1C9BFhs1Oq6FB0XkewD+G4DDSqlz9W3tAH4E4CwAbwH4K6XUuyIiANYAuAJAEsC1SqmX9c8sBXCHftg7lVKPVDo3w4PWqJT/CVp+SNMUjg2P4t3kKKa2J7BvIImTE00Y39JEb4o4DsPMnuNvTktELgYwCGB9gdG6B8CAUuqfRORLAE5WSt0uIlcA+HtkjdZcAGuUUnN1I7cVwBwACsA2ALOVUu+WOzeNVhYrf3SV9inbnNfjP+qsEU2VCEO6MLEtzsmEkPDjb05LKfUrAAMlm68EkPOUHgFwVcH29SrLiwBOEpEpAOYBeEYpNaAbqmcAXO7WmEuptU7DzzoPW+tqlcn/mL3vhyQ4WzjcV1I43Ge7cJj1N4SEF69zWqcqpQ4CgP7/ZH376QD2Fey3X99mtt11ap2U/a7zcLszhB+dJ5woHPb7eyGE1EZQhBhGbqEqs33sAURuFJGtIrL1yJEjNQ+o1knZj0m90INwuzOEH50nnBBisM0TIeHGa6N1SA/7Qf//sL59P4CpBfudAeBAme1jUEqtU0rNUUrNmTRpUs0DrXVS9npSL/Ug9h5Nuqq080PJ54Sii22eCAk3XhutXgBL9Z+XAvhZwfYlkuXDAP6khw83AbhMRE4WkZMBXKZvc51aJ2WvJ/VSD2L1M7uxalGna5JdPyTBTrSPomyekHDjpnpwA4BLAJwC4BCArwF4AsCPAUwDsBfAIqXUgC55/xayIoskgOuUUlv141wP4Cv6Ye9SSn2/0rmdUA/WKvf2Wi5uVMF/VddpuPOq87Jrarmg7gujJDhoMn5CSB62caqVWidlLyd19kqzThiNLSENANs41Uqt7YC8bCfECn7rsM0TIeGFj+B1QmG+hx4EIaReodGqI3IeBACGBAkhdQnDgwGH3RsIIeQEfBwPMFS6+QOFGoQEF3paAYbdG7zHjzZP9KYJsQ6NVoBh9wbv8fpBgb0QCbEHjVaAaYTuDUHzMrx+UKA3TYg9aLQCTL3XXgXRy/D6QYHeNCH2oNEKME702gsyQfQyvH5QaARvmhAnoXow4NRz7VUQvQyni7QrKRFzRrJUIVov3jQhTlNfsyBxHDfl3zkvo7BfYs7L8NNAO/WgYKVkgZ1MCLEHw4PEFLdzTvWes7Ma/mQvREKsQ0+LmFI46QLIT7pOdY6vdy8jiOFPQsIOPS1iiheTbj17GRRZEOI8NFrEFCcm3aDVYTlJpWur9/AnIX7ARSCJKWFbvdlNSgUprbEIBpKjFa+NfQwJsQxXLia1U8ukWy+rKRsZ3wc/Nxs3Pbot9NdGSICwNLHwr4uUpRb5d70IEYwEKW3xWF1cGyFhgzkt4hr1IkQwMr79hwfr4toICRs0WsQ16kWIYGR8N712EGu6u0J/bYSEDea0iKvUgxDBTFDSnmjC8bQW6msjJEBQiEGcoR4MT63wHhDiOhRikNqpJ9l6LdRz42JCwgRzWqQsQVw+hBDSuNBokbLUi2ydEFIf0GiRstSLbJ0QUh/QaDUwVvoC1otsnRBSHzCj3KBYFVjU+/IhhJBwQU+rQbEjsKjn5UMIIeGCRqtBocCCEBJGaLQaFAosCCFhhEarQaHAghASRijEaFAosCCEhBEarQaGrYkIIWGD4UFCCCGhgUaLEEJIaKDRIoQQEhpotAghhIQGGi1CCCGhgUaLEEJIaKDRIoQQEhpotAghhIQGGi1CCCGhgUaLEEJIaKDRIoQQEhpotAghhIQGUUr5PQbHEZEjAN72exwlnALgHb8HUYEwjBEIxzg5RmfgGJ0j6ON8Ryl1eaWd6tJoBRER2aqUmuP3OMoRhjEC4Rgnx+gMHKNzhGWclWB4kBBCSGig0SKEEBIaaLS8Y53fA7BAGMYIhGOcHKMzcIzOEZZxloU5LUIIIaGBnhYhhJDQQKPlMCJyuYjsEpF+EfmSwfsXi8jLIpIWkc8EdIwrROT3IrJDRH4pImcGcIxfEJFXRaRPRH4jIh/weoxWxlmw32dERImI5+otC/fyWhE5ot/LPhH526CNUd/nr/Tfy50i8i9BG6OIfLPgHu4WkT8GcIzTROQ5Edmu/31f4fUYa0YpxX8O/QMQBfAHANMBNAN4BcAHSvY5C0AngPUAPhPQMX4CQEL/+YsAfhTAMU4o+HkBgKeCeC/1/cYD+BWAFwHMCdoYAVwL4Fte3z+bY5wBYDuAk/XXk4M2xpL9/x7A94I2RmTzWl/Uf/4AgLf8+t6r/UdPy1kuBNCvlHpDKTUCYCOAKwt3UEq9pZTaAUDzY4CwNsbnlFJJ/eWLAM4I4BjfK3jZBsCP5GzFcep8A8A9AIa9HJyO1TH6iZUx3gDgfqXUuwCglDocwDEW0g1ggycjO4GVMSoAE/Sf3wfggIfjcwQaLWc5HcC+gtf79W1Bwu4YPw/gSVdHNBZLYxSRm0XkD8gahGUeja2QiuMUkfMBTFVK/ZuXAyvA6ve9UA8X/UREpnoztDxWxngOgHNE5Lci8qKIVOyc4DCW/270cPrZAJ71YFyFWBljD4BrRGQ/gF8g6xGGChotZxGDbUGTZ1oeo4hcA2AOgFWujsjg1AbbxoxRKXW/Uur9AG4HcIfroxpL2XGKSATANwH8D89GNBYr9/L/A3CWUqoTwGYAj7g+qmKsjDGGbIjwEmS9mO+IyEkuj6sQO3/biwH8RCmVcXE8RlgZYzeAh5VSZwC4AsCj+u9paAjVYEPAfgCFT6lnIHjut6UxisinAHwVwAKlVMqjseWwex83ArjK1REZU2mc4wGcC+B5EXkLwIcB9Hosxqh4L5VSRwu+44cAzPZobDmsfN/7AfxMKTWqlHoTwC5kjZhX2PmdXAzvQ4OAtTF+HsCPAUAp9QKAFmR7EoYHv5Nq9fQP2afBN5ANDeQSobNM9n0Y/ggxKo4RwPnIJnRnBPU+Fo4NwH8HsDWI4yzZ/3l4L8Swci+nFPz8fwF4MYBjvBzAI/rPpyAbBpsYpDHq+80E8Bb0GtgA3scnAVyr//wXyBo1z8da03X6PYB6+4esy71bn/S/qm/7OrIeCwB8CNknoiEARwHsDOAYNwM4BKBP/9cbwDGuAbBTH99z5YyFn+Ms2ddzo2XxXt6t38tX9Hv55wEcowBYDeD3AF4FsDhoY9Rf9wD4Jz9+Fy3exw8A+K3+XfcBuMyvsVb7jx0xCCGEhAbmtAghhIQGGi1CCCGhgUaLEEJIaKDRIoQQEhpotAghhIQGGi1CfEJEThKR/9vvcRASJmi0CPGPkwDQaBFiAxotQvzjnwC8X19/aZWI3CYiv9Mb1/5PABCRs0TkP0TkOyLymoj8UEQ+pTeO3SMiF+r79YjIoyLyrL79Bl+vjBCXoNEixD++BOAPSqkuAM8g20vvQgBdAGaLyMX6fh3IdgDpBPDnAP4awEcB3ArgKwXH6wTwlwAuAvD/ishpXlwEIV5Co0VIMLhM/7cdwMvIGqdcQ9g3lVKvKqU0ZNst/VJlW9m8iuyiojl+ppQ6rpR6B9l2TBd6NXhCvCLm9wAIIQCyvfXuVko9WLRR5CwAhV32tYLXGor/hkt7srFHG6k76GkR4h/HkF2+BAA2AbheRMYBgIicLiKTbR7vShFpEZGJyK479TvHRkpIQKCnRYhPKKWO6oKK15BdMuJfALwgIgAwCOAaAHYWEnwJwM8BTAPwDaVU0NZyI6Rm2OWdkDpARHoADCql7vV7LIS4CcODhBBCQgM9LUIIIaGBnhYhhJDQQKNFCCEkNNBoEUIICQ00WoQQQkIDjRYhhJDQQKNFCCEkNPz//xYye+O59UQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfcd8ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(train, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索完毕，将冗余项和无关的特征排除，并且对天气、月份和星期进行one-hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>...</th>\n",
       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  holiday  workingday      temp     atemp  \\\n",
       "0        1  2011-01-01       1   0        0           0  0.344167  0.363625   \n",
       "1        2  2011-01-02       1   0        0           0  0.363478  0.353739   \n",
       "2        3  2011-01-03       1   0        0           1  0.196364  0.189405   \n",
       "3        4  2011-01-04       1   0        0           1  0.200000  0.212122   \n",
       "4        5  2011-01-05       1   0        0           1  0.226957  0.229270   \n",
       "\n",
       "        hum  windspeed    ...      mnth_10  mnth_11  mnth_12  weekday_0  \\\n",
       "0  0.805833   0.160446    ...            0        0        0          0   \n",
       "1  0.696087   0.248539    ...            0        0        0          1   \n",
       "2  0.437273   0.248309    ...            0        0        0          0   \n",
       "3  0.590435   0.160296    ...            0        0        0          0   \n",
       "4  0.436957   0.186900    ...            0        0        0          0   \n",
       "\n",
       "   weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          1  \n",
       "1          0          0          0          0          0          0  \n",
       "2          1          0          0          0          0          0  \n",
       "3          0          1          0          0          0          0  \n",
       "4          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './'\n",
    "data = pd.read_csv(dpath +\"day.csv\")\n",
    "data['weathersit'] = data['weathersit'].astype('str')\n",
    "data = pd.get_dummies(data,columns=['weathersit','mnth','weekday'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train = data[data.yr==0]\n",
    "test = data[data.yr==1]\n",
    "\n",
    "uselessData = ['instant','dteday','casual','registered','season','temp']\n",
    "train = train.drop(uselessData, axis=1)\n",
    "test = test.drop(uselessData, axis=1)\n",
    "\n",
    "X = train.drop('cnt', axis = 1)\n",
    "X_test = test.drop('cnt', axis = 1)\n",
    "y = train['cnt'].values\n",
    "y_test = test['cnt'].values\n",
    "\n",
    "columns = X.columns\n",
    "\n",
    "y = y + np.mean(y_test)-np.mean(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于11年与12年y均值差异较大，考虑将11年的y值加上差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfa8a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_diff = y_test[:365] - y\n",
    "sns.distplot(y_diff, bins=30, kde=True)\n",
    "plt.xlabel('value of cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2012年与2011年y值得差异基本近似高斯分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scale_X = MinMaxScaler()\n",
    "scale_y = MinMaxScaler()\n",
    "# 对训练数据的特征进行归一化处理\n",
    "normalizeColumns = ['atemp','hum','windspeed']\n",
    "normalizeX = X[normalizeColumns]\n",
    "normalizeX = scale_X.fit_transform(normalizeX)\n",
    "X[normalizeColumns] = normalizeX\n",
    "X_test[normalizeColumns] = scale_X.transform(X_test[normalizeColumns])\n",
    "y = scale_y.fit_transform(y.reshape(-1, 1))\n",
    "y_test = scale_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6284263130341422\n",
      "The r2 score of RidgeCV on train is 0.8202463593617635\n"
     ]
    }
   ],
   "source": [
    "#岭回归\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "ridge.fit(X, y)    \n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfcf0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_test - y_test_pred_ridge,bins=40, label='Ridge', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接近正态分布，左侧疑似有噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1277b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_test, y_test_pred_ridge)\n",
    "plt.plot([-1, 1], [-1, 1], '--k') \n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1277bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.36764540068682594]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[0.13760317696021968]</td>\n",
       "      <td>mnth_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[0.13104670493094603]</td>\n",
       "      <td>mnth_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.1269955087157934]</td>\n",
       "      <td>mnth_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.12519933544009637]</td>\n",
       "      <td>mnth_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.11119269187496042]</td>\n",
       "      <td>weathersit_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.06509468253606526]</td>\n",
       "      <td>mnth_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.0537262323484717]</td>\n",
       "      <td>weathersit_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.0486342066169074]</td>\n",
       "      <td>mnth_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[0.0470141636590977]</td>\n",
       "      <td>mnth_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.025827171249007297]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[0.021617897168346834]</td>\n",
       "      <td>weekday_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[0.002955993312064531]</td>\n",
       "      <td>weekday_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[-0.00010132894982201046]</td>\n",
       "      <td>weekday_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[-0.00015035766462201394]</td>\n",
       "      <td>weekday_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[-0.003386409039564997]</td>\n",
       "      <td>weekday_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[-0.008965465976832239]</td>\n",
       "      <td>mnth_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[-0.010404512833424945]</td>\n",
       "      <td>weekday_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[-0.010531281992976593]</td>\n",
       "      <td>weekday_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[-0.03929333848915262]</td>\n",
       "      <td>mnth_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-0.04734373946753151]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.1540250647886659]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.15746285783549432]</td>\n",
       "      <td>mnth_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.16491892422343182]</td>\n",
       "      <td>weathersit_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.1722328429187311]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.21803194267816162]</td>\n",
       "      <td>mnth_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.25783417387948526]</td>\n",
       "      <td>mnth_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   coef_ridge       columns\n",
       "3       [0.36764540068682594]         atemp\n",
       "17      [0.13760317696021968]        mnth_9\n",
       "13      [0.13104670493094603]        mnth_5\n",
       "14       [0.1269955087157934]        mnth_6\n",
       "18      [0.12519933544009637]       mnth_10\n",
       "6       [0.11119269187496042]  weathersit_1\n",
       "16      [0.06509468253606526]        mnth_8\n",
       "7        [0.0537262323484717]  weathersit_2\n",
       "15       [0.0486342066169074]        mnth_7\n",
       "19       [0.0470141636590977]       mnth_11\n",
       "2      [0.025827171249007297]    workingday\n",
       "27     [0.021617897168346834]     weekday_6\n",
       "23     [0.002955993312064531]     weekday_2\n",
       "0                       [0.0]            yr\n",
       "21  [-0.00010132894982201046]     weekday_0\n",
       "26  [-0.00015035766462201394]     weekday_5\n",
       "22    [-0.003386409039564997]     weekday_1\n",
       "12    [-0.008965465976832239]        mnth_4\n",
       "24    [-0.010404512833424945]     weekday_3\n",
       "25    [-0.010531281992976593]     weekday_4\n",
       "20     [-0.03929333848915262]       mnth_12\n",
       "1      [-0.04734373946753151]       holiday\n",
       "5       [-0.1540250647886659]     windspeed\n",
       "11     [-0.15746285783549432]        mnth_3\n",
       "8      [-0.16491892422343182]  weathersit_3\n",
       "4       [-0.1722328429187311]           hum\n",
       "10     [-0.21803194267816162]        mnth_2\n",
       "9      [-0.25783417387948526]        mnth_1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\dev\\ai\\anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6368340373325572\n",
      "The r2 score of LassoCV on train is 0.8216987291579888\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "eps = 0.0001\n",
    "n_alphas = 1000\n",
    "\n",
    "lasso = LassoCV(eps = eps,n_alphas = n_alphas,max_iter=5000,tol=0.00001)  \n",
    "\n",
    "lasso.fit(X, y)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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We2lZCYeP13HHh0ZGXUraSfbo4CgQI34Do1uJD8GR+NJvEZEUicWcZ97cxlnDejG1oHfU5aSdZMPieeDLxMdqioVXjojIyflr8QE276/i4RvOVsd2CJINi/3u/rtQKxEROQXPvLmN/t1zuXLS4KhLSUvJhsUDZvYE8GfgeMNCd38plKpERFph097DLFy/jy9cMpbcrMyoy0lLyYbFZ4DxQDbvn4ZyQGEhIpF79PXNdM3O5HZ1bIcm2bA4290nhVqJiMhJKKk4ytwVu7ntvBH0zcuJupy0lexXZ982M12EJyLtzuN/2YIZfPaC0VGXktaSPbL4MHC7mW0l3mdhgLv7WaFVJiKSwP7Dx5m9ZCefnDKUIb27Rl1OWks2LC4PtQoRkZPwxKIt1NTH+NxHTou6lLSX7BDl28MuRESkNfYdrubZt7ZxzeShnJbfPepy0l6yfRYiIu3KI69uprbe+cIlY6MupVNQWIhIh7P74DF+/s4Orps6jJH9dZfnVFBYiEiH86NXi3Gcf7lkTNSldBoKCxHpUHaUHeXFJTu5cXoBw/p0i7qcTkNhISIdyg8WbiIzw7jnYh1VpJLCQkQ6jM37j/DSshJuO3cEA3t2ibqcTkVhISIdxrf/sJ6u2Zn840W6riLVFBYi0iG8UXyABWv38vmLx+iWqRFQWIhIu1cfcx76/VqG9enKneePirqcTklhISLt3otFO1m/5zBfu3ICXbJ1v4ooKCxEpF2rqKrhu/M3MGNkX644c1DU5XRaCgsRade+OW8dlcdqeeiaM3Vv7QgpLESk3Xqz+ABzlpbwuY+MZtygHlGX06kpLESkXaquree+37zLyH7d+JeLNVhg1JK9n4WISEo9vGAjWw9U8fN/OEed2u1AqEcWZna5mW0ws2Izu7eZ13PN7IXg9XfMbGSw/BYzW9HoETOzyWHWKiLtx8qdB3l80RZumjGcD43pH3U5QohhYWaZwCzgCmAicFMz9/G+C6hw9zHAw8B3ANz9eXef7O6TgduAbe6+IqxaRaT9OF5Xz1fmrGRAjy589coJUZcjgTCPLGYAxe6+xd1rgNnAzCZtZgLPBtNzgEvsb7/ucBPwixDrFJF2ZNarm9m49wjfuvZMenbJjrocCYQZFkOBnY3mS4JlzbZx9zrgENCvSZsbaCEszOxuMysys6L9+/e3SdEiEp13dx3ikVeLuXbKUC4ePzDqcqSRMMOiuS9Ee2vamNk5wFF3f7e5Dbj7Y+5e6O6F+fn5J1+piESupi7GV+asok9eDvdf1fSMtUQtzLAoAYY3mh8G7G6pjZllAb2A8kav34hOQYl0Co++tpl1pZV885oz6d0tJ+pypIkww2IJMNbMRplZDvEP/rlN2swFbg+mrwMWursDmFkGcD3xvg4RSWOrSg7yw4WbuPrsIVx2hob0aI9Cu87C3evM7B5gPpAJPOXua8zsG0CRu88FngSeM7Ni4kcUNzb6ERcCJe6+JawaRSR6ldW13PPz5eT3yOXrV58RdTnSglAvynP3ecC8JsvubzRdTfzoobl1XwPODbM+EYmWu3Pvr1ax6+AxXrj7XPrk6fRTe6XhPkQkMs+9vZ15q/fwb5edTuHIvlGXIyegsBCRSCzfUcFDv1/LxeMH8I8X6jap7Z3CQkRSrqKqhs8/v4wBPbrw/U+fTUaGhh5v7zSQoIikVCzmfPGFFRw4UsOcfzpPX5PtIHRkISIp9ejrm3l9437uv2oiZw3rHXU5kiSFhYikzLrSSv7nTxv5+FmDueWcgqjLkVZQWIhIStTWx/jyL1fSq2sO/zFTt0jtaNRnISIp8cirm1mzu5Kf3DZN11N0QDqyEJHQrd1dyQ8XbmLm5CF8TMN5dEgKCxEJVcPpp97dcnjwKg3n0VHpNJSIhGrWq8WsLa3kMZ1+6tB0ZCEioVldcogfLSzmmskaTbajU1iISCgWby3nrmeX0L97Lg9qNNkOT2EhIm2qPuY8+tpmbnr8bfJys3jmzum6SjsNqM9CRNrMu7sO8dWXVrN61yE+Pmkw3/7UJHp0yY66LGkDCgsROWWHjtXy8IKN/PStbfTNy2XWzVO5ctIgXXiXRhQWInJKth2o4ubH32ZPZTW3nDOCL182jl7ddDSRbhQWInLSivcd4ebH36Yu5rz0z+czebgGBkxXCgsROSnrSiu57cnFgPOLz57LuEE9oi5JQqRvQ4lIq72+cT/X//gtsjKM2XcrKDoDHVmISKvMXryD+37zLmMHdOfpz0xncK+uUZckKaCwEJGk1Mec//rjen7yly1cMLY/j9wyVV+L7UQUFiKSUNXxOr74wgoWrN3LbeeO4IGrJpKVqbPYnYnCQkROaEfZUT73s6Vs2FPJg1dN5I7zR0VdkkRAYSEiLXptwz6+MHsF7s5Td0znonEDoi5JIqKwEJG/UV1bz8MLNvLYoi2MG9iDn9w2jRH98qIuSyKksBCRD3h31yH+9cUVbNx7hJtmFHD/JybSNScz6rIkYgoLEQHgyPE6/mfBRp5+cxv98nJ4+jPT+ahOO0lAYSHSycVizu9W7eZb89ax7/Bxbpw+nP97+XgNKy4foLAQ6aTcnT+t28f3XtnA+j2HOXNoT3586zSmFPSJujRphxQWIp1MXX2Mee/u4YlFW1hVcoiR/brxg5um8IlJg8nI0JDi0jyFhUgnse9wNb9etoufvrWdXQePMap/Ht++dhKfmjaMbF1gJwkoLETSWHVtPa9t2MecpSW8umE/9TFnxqi+PHj1GVwyfoCOJCRpCguRNHPoWC2vrt/H/DV7eG3Dfo7V1jOgRy6fvWA0100bxpgB3aMuUTqgUMPCzC4H/hfIBJ5w9283eT0X+CkwDSgDbnD3bcFrZwE/AXoCMWC6u1eHWa9IR+TubNp3hNc37Of1jft5Z2sZtfXOgB65fGraUD52xiDOG91PYznJKQktLMwsE5gFXAqUAEvMbK67r23U7C6gwt3HmNmNwHeAG8wsC/gZcJu7rzSzfkBtWLWKdCTuzrayo7yzpYy3t5Tx9pZy9lTG/446fWB37jx/FJedMYgpw3vrNJO0mTCPLGYAxe6+BcDMZgMzgcZhMRN4MJieA/zI4nd4vwxY5e4rAdy9LMQ6RTqEXQeP8dxb2/nN8l3vhUP/7rmcM7ovF4zpz4Wn5zOkt+4tIeEIMyyGAjsbzZcA57TUxt3rzOwQ0A84HXAzmw/kA7Pd/b9CrFWkXaqPOW9tLuNnb2/nlbV7ALhkwkDuOX0M547ux2n5ecT/vhIJV5hh0dz/YE+yTRbwYWA6cBT4s5ktdfc/f2Bls7uBuwEKCgpOuWCR9mLt7kp+s2IXv12xi72Vx+ndLZu7LzyNW88tYFifblGXJ51QmGFRAgxvND8M2N1Cm5Kgn6IXUB4sf93dDwCY2TxgKvCBsHD3x4DHAAoLC5sGkUiH4u7MWVrCM29uY83uSrIyjIvGDeD+TwzlkgkD6JKtwfwkOmGGxRJgrJmNAnYBNwI3N2kzF7gdeAu4Dljo7g2nn/7dzLoBNcBHgIdDrFUkUnsrq/nKnFX8ZeN+JgzuydevPoOrzh5C3zyNzyTtQ2hhEfRB3APMJ/7V2afcfY2ZfQMocve5wJPAc2ZWTPyI4sZg3Qoz+z7xwHFgnru/HFatIlGat7qUr/16NdW19Tx0zZncek6B+iGk3TH39Dh7U1hY6EVFRVGXIZK0yupaHvztGl5avouzh/Xi4RsmMzpfF8xJagX9wYWJ2ukKbpEIvLW5jC//ciV7Kqv5wiVjuefiMRqfSdo1hYVICh2vq+d7r2zk8UVbGNkvjzn/eJ6GBJcOQWEhkiLr91TyxdkrWL/nMLecU8B9H59Atxy9BaVj0P9UkZAdranjRwuLeXzRFnp1zeHpO6bz0fG6Xal0LAoLkZC4O398dw8P/X4tuw9V86mpw/jalePp1z036tJEWk1hIdLG3J03isv471c2sGLnQcYP6sH/3jSF6SP7Rl2ayElTWIi0EXfnzc1l/HDhJt7eUs6QXl349rWTuG7aMA0PLh2ewkLkFB2vq+d3K0t5YtEW1u85TP/uuTxw1URuPqeA3CwN0SHpQWEhcpLWlVbyy6ISfrNiF+VVNYwb2IP/uu4srj57iMZxkrSjsBBphR1lR5m/Zg+/XbmLd3dVkp1pXDpxIDfNKODDY/prmA5JWwoLkROIxZw1uyt5dcM+/vjuHtaWVgJw5tCePHDVRGZOHqrB/qRTUFiINOLubC87yttbylhUfIA3iw9QcTR+R99pI/pw35UT+NgZgyjop3tKSOeisJBOrbK6ljW7Klm2o4LlOypYvuMgZVU1AAzokctHxw/ggrH9OX9Mfwb06BJxtSLRUVhIp1BdW8/O8qNs2HuY9aWHWb+nknWlh9l18Nh7bUbn5/HR8QOYUtCb6SP7MnZAd/VBiAQUFpIWYjGn/GgNpQer2X3oGDvKjrK1rIrtZVVsO3CU3YeO0TAaf2aGcVp+HtNG9OGWcwuYMLgnU4b3pnc39T2ItERhIe3a0Zo6yo7UUF4Vf5RV1VBedZyyqhr2HKqm9FA1pYeOsffQcWrqYx9Yt3e3bEb2y2P6yD6M7D+Mkf3yGDuwO2MGdNf1DyKtpLCQNufu1NY7x2rqOVYbPGrqOVZbx5Hj9RyuruVwdV2j5zoqq2upPPb+skPHaimrOk51bazZbeRkZjCwVy6De3VlakEfBvXqwpBeXRnUqwuDe3WhoG83HSmItCGFRcTcHXeIueMEz857yxqWeyw+X+9Ofez9R13sg/PxZTFi7tTVv9++LubEmmlfF3Nq6mLU1scfx4PpmkbPNfXezLL4c3VdjOq/CYV66mPJ3YHRDLrnZtGzSzY9umTRo0sWg3p1YfygHvTNy6Fv9xz65eXQNy+Xfu9N59A9N0v9CSIp1OnDYv2eSj7//DIgfrNvPP7sDR/SwWee8/6HODT6IHeI+fvtY40//IPlsUZh0DDf8NxeZWUY2ZkZ5GRlkJ2ZQW5WBtmZ9t58dmYGOZk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e81748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 4.209705378978875e-06)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.423461</td>\n",
       "      <td>[0.36764540068682594]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.086885</td>\n",
       "      <td>[0.13760317696021968]</td>\n",
       "      <td>mnth_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.080047</td>\n",
       "      <td>[0.13104670493094603]</td>\n",
       "      <td>mnth_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.080026</td>\n",
       "      <td>[0.12519933544009637]</td>\n",
       "      <td>mnth_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.061313</td>\n",
       "      <td>[0.1269955087157934]</td>\n",
       "      <td>mnth_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.046310</td>\n",
       "      <td>[0.11119269187496042]</td>\n",
       "      <td>weathersit_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.005005</td>\n",
       "      <td>[0.002955993312064531]</td>\n",
       "      <td>weekday_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004871</td>\n",
       "      <td>[0.025827171249007297]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.004773</td>\n",
       "      <td>[0.021617897168346834]</td>\n",
       "      <td>weekday_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.003142</td>\n",
       "      <td>[0.0470141636590977]</td>\n",
       "      <td>mnth_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.001987</td>\n",
       "      <td>[-0.00015035766462201394]</td>\n",
       "      <td>weekday_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.003386409039564997]</td>\n",
       "      <td>weekday_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.06509468253606526]</td>\n",
       "      <td>mnth_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[0.0537262323484717]</td>\n",
       "      <td>weathersit_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.005890</td>\n",
       "      <td>[-0.010404512833424945]</td>\n",
       "      <td>weekday_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.009086</td>\n",
       "      <td>[-0.010531281992976593]</td>\n",
       "      <td>weekday_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.017142</td>\n",
       "      <td>[-0.00010132894982201046]</td>\n",
       "      <td>weekday_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.022744</td>\n",
       "      <td>[0.0486342066169074]</td>\n",
       "      <td>mnth_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.058913</td>\n",
       "      <td>[-0.008965465976832239]</td>\n",
       "      <td>mnth_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.076541</td>\n",
       "      <td>[-0.04734373946753151]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.083865</td>\n",
       "      <td>[-0.03929333848915262]</td>\n",
       "      <td>mnth_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.178318</td>\n",
       "      <td>[-0.1540250647886659]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.209112</td>\n",
       "      <td>[-0.15746285783549432]</td>\n",
       "      <td>mnth_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.223067</td>\n",
       "      <td>[-0.16491892422343182]</td>\n",
       "      <td>weathersit_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.238898</td>\n",
       "      <td>[-0.1722328429187311]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.268797</td>\n",
       "      <td>[-0.21803194267816162]</td>\n",
       "      <td>mnth_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.304684</td>\n",
       "      <td>[-0.25783417387948526]</td>\n",
       "      <td>mnth_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso                 coef_ridge       columns\n",
       "3     0.423461      [0.36764540068682594]         atemp\n",
       "17    0.086885      [0.13760317696021968]        mnth_9\n",
       "13    0.080047      [0.13104670493094603]        mnth_5\n",
       "18    0.080026      [0.12519933544009637]       mnth_10\n",
       "14    0.061313       [0.1269955087157934]        mnth_6\n",
       "6     0.046310      [0.11119269187496042]  weathersit_1\n",
       "23    0.005005     [0.002955993312064531]     weekday_2\n",
       "2     0.004871     [0.025827171249007297]    workingday\n",
       "27    0.004773     [0.021617897168346834]     weekday_6\n",
       "19    0.003142       [0.0470141636590977]       mnth_11\n",
       "26    0.001987  [-0.00015035766462201394]     weekday_5\n",
       "0     0.000000                      [0.0]            yr\n",
       "22    0.000000    [-0.003386409039564997]     weekday_1\n",
       "16    0.000000      [0.06509468253606526]        mnth_8\n",
       "7    -0.000000       [0.0537262323484717]  weathersit_2\n",
       "24   -0.005890    [-0.010404512833424945]     weekday_3\n",
       "25   -0.009086    [-0.010531281992976593]     weekday_4\n",
       "21   -0.017142  [-0.00010132894982201046]     weekday_0\n",
       "15   -0.022744       [0.0486342066169074]        mnth_7\n",
       "12   -0.058913    [-0.008965465976832239]        mnth_4\n",
       "1    -0.076541     [-0.04734373946753151]       holiday\n",
       "20   -0.083865     [-0.03929333848915262]       mnth_12\n",
       "5    -0.178318      [-0.1540250647886659]     windspeed\n",
       "11   -0.209112     [-0.15746285783549432]        mnth_3\n",
       "8    -0.223067     [-0.16491892422343182]  weathersit_3\n",
       "4    -0.238898      [-0.1722328429187311]           hum\n",
       "10   -0.268797     [-0.21803194267816162]        mnth_2\n",
       "9    -0.304684     [-0.25783417387948526]        mnth_1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lasso'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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